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TheFuriousGunner

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[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Yolo, California, USA, along the US50-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 15, 19, 44, 98, 150, 156, 178, 208, 246, 248, 257 and 263, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [269, 262, 229, 221, 214, 152, 127, 100, 58, 38, 25, 22]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 24, 42, 124, 250, 332, 296, 287, 307, 334, 359, 376 and 381, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [384, 366, 317, 282, 242, 194, 164, 111, 76, 46, 30, 18]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 3, direction of eastbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 12, 15, 39, 91, 134, 138, 142, 166, 198, 212, 226 and 242, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [260, 264, 232, 191, 141, 112, 80, 58, 35, 21, 17, 11]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Yolo, California, USA, along the US50-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 47, 85, 140, 195, 220, 222, 262, 304, 304, 326, 315 and 326, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [286, 284, 248, 228, 253, 231, 161, 116, 57, 35, 30, 36]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in El Dorado, California, USA, along the US50-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 20, 30, 60, 105, 128, 138, 156, 158, 167, 167, 165 and 162, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [177, 163, 158, 126, 103, 85, 62, 50, 27, 16, 12, 14]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 42, 62, 136, 254, 314, 330, 387, 577, 581, 581, 616 and 646, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [576, 488, 420, 385, 332, 256, 220, 155, 107, 69, 48, 40]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 29, 36, 83, 164, 207, 205, 231, 279, 300, 320, 332 and 342, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [328, 314, 307, 269, 241, 188, 169, 112, 74, 47, 69, 31]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 54, 71, 135, 250, 256, 267, 320, 350, 378, 404, 371 and 378, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [374, 359, 365, 344, 314, 246, 198, 136, 95, 68, 54, 50]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Placer, California, USA, along the I80-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 19, 34, 65, 125, 157, 192, 238, 288, 356, 343, 344 and 352, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [340, 292, 291, 282, 271, 237, 202, 67, 35, 26, 19, 17]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 48, 78, 153, 272, 270, 278, 318, 370, 384, 384, 400 and 408, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [371, 335, 325, 296, 238, 190, 164, 120, 82, 54, 42, 40]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 46, 49, 64, 90, 111, 122, 137, 141, 156, 164, 163 and 160, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [160, 156, 156, 151, 137, 116, 99, 78, 66, 52, 47, 46]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 5.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 19, 40, 63, 103, 107, 94, 106, 112, 114, 119, 117 and 112, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [112, 116, 106, 95, 65, 49, 38, 22, 14, 11, 9, 11]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 2, 105, 218, 273, 228, 293, 337, 391, 361, 400, 425 and 416, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [378, 394, 377, 334, 264, 221, 193, 133, 17, 2, 1, 1]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Solano, California, USA, along the I80-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 18, 28, 79, 156, 244, 256, 228, 229, 250, 276, 293 and 318, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [294, 274, 288, 281, 229, 169, 144, 106, 64, 37, 23, 20]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 38, 78, 146, 171, 166, 169, 202, 235, 259, 268, 258 and 274, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [272, 283, 293, 284, 288, 200, 173, 120, 76, 52, 36, 34]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in San Mateo, California, USA, along the US101-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 8.3\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 66, 126, 237, 322, 384, 410, 456, 495, 496, 539, 532 and 530, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [518, 518, 491, 448, 406, 402, 371, 252, 148, 99, 58, 47]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Marin, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 68, 70, 87, 120, 187, 245, 278, 319, 349, 349, 350 and 355, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [364, 352, 330, 285, 244, 216, 190, 164, 120, 98, 81, 68]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 14, 25, 66, 118, 176, 180, 176, 175, 185, 196, 197 and 218, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [212, 209, 207, 204, 172, 137, 115, 86, 60, 35, 19, 16]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Marin, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 73, 89, 126, 211, 239, 250, 288, 307, 325, 311, 324 and 316, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [331, 356, 328, 279, 216, 177, 158, 120, 91, 75, 68, 66]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in San Mateo, California, USA, along the I280-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 8.3\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 26, 33, 76, 158, 228, 270, 305, 377, 402, 395, 413 and 434, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [470, 520, 452, 375, 318, 285, 255, 194, 130, 86, 53, 31]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I580-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 24, 38, 106, 208, 324, 342, 303, 306, 333, 367, 390 and 424, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [393, 365, 384, 375, 306, 226, 191, 142, 85, 49, 31, 27]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I680-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 62, 74, 110, 157, 182, 195, 213, 229, 237, 240, 244 and 246, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [247, 234, 228, 208, 190, 165, 134, 105, 81, 66, 57, 55]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the I680-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 137, 162, 196, 278, 382, 449, 498, 622, 682, 732, 750 and 810, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [803, 809, 699, 607, 532, 478, 420, 341, 304, 229, 219, 231]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the I680-S freeway, lane 4, direction of southbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 25, 49, 109, 160, 185, 226, 273, 335, 352, 368, 373 and 414, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [399, 403, 383, 370, 292, 247, 205, 161, 96, 48, 29, 24]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the I680-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 79, 119, 193, 278, 337, 350, 404, 455, 456, 466, 470 and 478, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [492, 484, 433, 386, 325, 288, 232, 188, 128, 94, 74, 77]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the I880-N freeway, lane 6, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 77, 95, 176, 246, 298, 313, 380, 448, 514, 561, 572 and 601, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [597, 584, 573, 514, 486, 394, 321, 229, 170, 104, 70, 62]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I880-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 8.1\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 48, 96, 186, 280, 282, 308, 384, 418, 497, 470, 475 and 468, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [453, 452, 430, 370, 310, 262, 216, 168, 112, 69, 40, 40]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 5 in Santa Cruz, California, USA, along the SR17-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 10, 31, 76, 117, 126, 0, 102, 244, 258, 263, 276 and 300, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [308, 300, 280, 52, 0, 0, 0, 10, 26, 15, 8, 10]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 6 in Kern, California, USA, along the SR99-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 9.9\\u00b0C, and visibility reaches 9.9 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 38, 61, 165, 210, 146, 145, 147, 190, 212, 216, 258 and 260, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [278, 284, 226, 190, 142, 114, 83, 76, 46, 49, 29, 27]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 94, 114, 186, 263, 285, 306, 315, 302, 314, 332, 305 and 328, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [343, 364, 339, 284, 217, 176, 171, 139, 112, 98, 80, 88]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 11, 30, 86, 173, 208, 204, 196, 182, 170, 167, 178 and 185, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [226, 226, 197, 156, 107, 75, 70, 48, 27, 16, 9, 7]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I5-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 62, 132, 378, 552, 572, 542, 513, 506, 508, 521, 530 and 560, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [575, 572, 568, 522, 440, 338, 306, 238, 153, 99, 74, 78]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I10-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 44, 91, 262, 414, 482, 456, 399, 390, 397, 412, 423 and 457, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [476, 481, 482, 437, 358, 267, 238, 185, 117, 77, 58, 53]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 254, 352, 390, 390, 418, 414, 428, 449, 454, 404, 453 and 464, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [448, 465, 458, 436, 386, 304, 270, 228, 195, 174, 160, 162]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR47-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 11, 6, 15, 38, 54, 60, 56, 60, 84, 61, 69 and 82, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [87, 114, 86, 70, 52, 43, 51, 32, 23, 19, 18, 39]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 78, 126, 222, 297, 364, 346, 353, 408, 438, 492, 501 and 489, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [478, 478, 510, 446, 405, 339, 279, 214, 143, 101, 61, 57]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 5, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 177, 231, 299, 374, 450, 472, 450, 487, 505, 541, 560 and 573, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [564, 556, 588, 531, 448, 416, 358, 305, 227, 187, 149, 151]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 115, 184, 278, 357, 434, 452, 453, 476, 469, 504, 537 and 544, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [460, 488, 480, 465, 424, 388, 348, 286, 198, 164, 117, 120]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 68, 111, 212, 318, 379, 364, 351, 366, 392, 411, 459 and 480, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [488, 508, 495, 440, 381, 350, 300, 228, 144, 93, 65, 63]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR91-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 44, 91, 262, 414, 482, 456, 399, 390, 397, 412, 423 and 457, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [476, 481, 482, 437, 358, 267, 238, 185, 117, 77, 52, 53]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 37, 80, 174, 298, 391, 365, 385, 460, 453, 471, 462 and 439, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [475, 472, 452, 391, 327, 289, 253, 183, 110, 67, 36, 28]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 138, 138, 254, 323, 295, 322, 372, 398, 400, 392, 378 and 372, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [371, 376, 392, 363, 346, 344, 342, 282, 200, 137, 89, 75]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I110-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 134, 218, 408, 454, 449, 466, 498, 504, 504, 462, 441 and 482, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [452, 487, 493, 476, 466, 414, 387, 295, 236, 173, 118, 104]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 37, 55, 124, 256, 324, 338, 392, 462, 469, 480, 486 and 466, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [453, 421, 419, 406, 381, 303, 261, 194, 123, 74, 38, 32]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 83, 92, 132, 200, 290, 304, 310, 324, 358, 356, 354 and 358, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [358, 374, 364, 333, 300, 248, 208, 174, 133, 112, 84, 87]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I210-E freeway, lane 5, direction of eastbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 75, 99, 174, 306, 424, 440, 500, 551, 597, 610, 563 and 632, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [628, 602, 590, 526, 473, 372, 311, 225, 164, 114, 72, 72]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 102, 240, 500, 515, 552, 551, 574, 680, 674, 688, 655 and 673, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [712, 649, 673, 636, 551, 485, 424, 311, 200, 126, 80, 72]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 144, 261, 513, 543, 567, 557, 566, 658, 659, 684, 651 and 671, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [675, 662, 680, 628, 537, 488, 426, 330, 233, 160, 119, 118]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 44, 80, 187, 288, 275, 312, 333, 367, 362, 364, 374 and 387, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [309, 315, 312, 337, 322, 309, 266, 230, 161, 101, 52, 34]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 141, 161, 254, 363, 446, 497, 562, 604, 614, 620, 648 and 637, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [633, 639, 597, 565, 550, 506, 446, 393, 306, 232, 146, 132]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 6, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 81, 104, 219, 365, 476, 536, 615, 682, 685, 714, 763 and 752, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [770, 739, 692, 669, 664, 567, 509, 411, 292, 197, 93, 68]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 51, 72, 160, 250, 346, 362, 370, 406, 448, 468, 502 and 487, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [418, 448, 456, 427, 404, 348, 307, 244, 178, 110, 56, 44]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 57, 120, 335, 478, 576, 560, 516, 506, 521, 550, 561 and 550, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [497, 458, 465, 457, 542, 464, 368, 282, 185, 127, 69, 58]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 89, 149, 336, 575, 594, 624, 667, 693, 680, 702, 699 and 672, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [494, 528, 611, 637, 624, 574, 545, 455, 309, 198, 98, 67]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 36, 70, 166, 241, 308, 342, 378, 388, 424, 432, 403 and 508, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [523, 484, 523, 439, 326, 265, 234, 154, 82, 49, 32, 28]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 72, 126, 242, 382, 444, 457, 476, 495, 520, 534, 552 and 547, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [513, 542, 543, 488, 420, 379, 335, 259, 178, 116, 78, 70]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 48, 80, 184, 282, 330, 332, 348, 378, 418, 448, 434 and 370, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [343, 354, 316, 285, 289, 254, 209, 157, 102, 68, 45, 42]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 93, 144, 272, 332, 324, 290, 304, 327, 338, 352, 334 and 364, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [380, 366, 383, 322, 287, 234, 225, 184, 140, 114, 81, 84]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 44, 91, 262, 414, 481, 456, 399, 390, 397, 412, 423 and 458, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [476, 481, 482, 437, 358, 267, 238, 186, 117, 78, 52, 53]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 44, 91, 262, 414, 482, 456, 399, 390, 397, 412, 423 and 457, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [476, 481, 482, 437, 358, 267, 238, 185, 117, 77, 52, 53]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 28, 57, 168, 277, 325, 302, 254, 244, 246, 253, 258 and 286, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [322, 337, 339, 287, 214, 158, 142, 109, 70, 48, 34, 36]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 109, 262, 302, 326, 370, 377, 416, 451, 468, 258, 466 and 496, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [474, 516, 492, 438, 358, 317, 275, 211, 154, 103, 81, 86]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 147, 339, 416, 433, 420, 378, 416, 464, 481, 482, 459 and 488, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [436, 418, 386, 365, 320, 279, 226, 145, 103, 67, 62, 77]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 103, 216, 298, 382, 349, 334, 367, 422, 436, 441, 448 and 485, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [466, 448, 409, 363, 302, 268, 221, 148, 106, 70, 60, 72]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 79, 165, 344, 433, 476, 500, 468, 469, 478, 491, 506 and 551, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [591, 570, 589, 500, 396, 335, 294, 233, 170, 112, 80, 72]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 95, 104, 112, 146, 185, 214, 247, 274, 282, 294, 290 and 282, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [300, 284, 263, 212, 189, 174, 152, 134, 111, 98, 99, 98]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 122, 138, 172, 216, 248, 287, 326, 372, 383, 355, 348 and 353, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [298, 306, 340, 368, 378, 269, 230, 195, 156, 128, 118, 109]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 100, 120, 152, 194, 212, 229, 249, 264, 275, 284, 313 and 344, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [315, 327, 370, 335, 306, 260, 240, 208, 169, 150, 107, 88]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 42, 64, 102, 143, 193, 209, 250, 290, 323, 327, 366 and 384, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [394, 418, 450, 352, 277, 238, 194, 155, 106, 80, 56, 56]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the SR60-E freeway, lane 2, direction of eastbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 33, 55, 90, 136, 150, 176, 208, 244, 250, 244, 242 and 254, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [273, 214, 240, 241, 214, 186, 161, 125, 94, 63, 52, 39]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the SR71-S freeway, lane 3, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 32, 60, 116, 158, 206, 213, 209, 230, 246, 251, 290 and 278, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [284, 284, 319, 273, 215, 174, 147, 106, 60, 39, 26, 26]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the SR91-W freeway, lane 3, direction of westbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 88, 182, 183, 270, 332, 316, 328, 350, 360, 364, 374 and 386, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [361, 365, 358, 336, 289, 256, 201, 156, 102, 66, 47, 48]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I210-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 100, 234, 241, 272, 291, 291, 314, 329, 345, 364, 378 and 374, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [349, 348, 308, 327, 274, 238, 202, 161, 116, 68, 58, 63]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 39, 73, 146, 220, 237, 254, 259, 286, 291, 303, 318 and 336, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [345, 338, 339, 284, 227, 190, 152, 119, 73, 49, 32, 25]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 5, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 58, 92, 156, 207, 266, 289, 316, 343, 370, 410, 440 and 480, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [548, 548, 541, 429, 355, 274, 236, 180, 134, 93, 63, 59]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I215-S freeway, lane 3, direction of southbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 131, 186, 179, 251, 273, 260, 266, 278, 294, 279, 276 and 285, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [284, 275, 300, 299, 258, 233, 210, 180, 153, 122, 110, 116]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-S freeway, lane 2, direction of southbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 40, 82, 172, 216, 238, 250, 234, 234, 239, 246, 254 and 276, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [296, 286, 295, 250, 198, 167, 147, 116, 86, 56, 40, 36]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Merced, California, USA, along the I5-N freeway, lane 2, direction of northbound. - Today's weather: Sunny. Temperature is 6.7\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 36, 34, 32, 39, 56, 70, 103, 111, 129, 152, 172 and 198, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [187, 202, 219, 223, 230, 225, 154, 138, 113, 76, 54, 49]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in San Joaquin, California, USA, along the I5-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 7.7\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 36, 46, 58, 58, 65, 74, 81, 97, 99, 112, 113 and 134, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [160, 130, 130, 115, 88, 87, 78, 64, 53, 45, 37, 33]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Merced, California, USA, along the SR99-N freeway, lane 2, direction of northbound. - Today's weather: Sunny. Temperature is 6.7\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 32, 49, 86, 106, 101, 116, 125, 150, 172, 179, 191 and 184, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [190, 196, 180, 151, 134, 112, 98, 75, 60, 46, 32, 36]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Stanislaus, California, USA, along the SR99-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 7.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 89, 138, 216, 285, 246, 250, 269, 305, 326, 363, 381 and 364, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [353, 362, 361, 295, 258, 211, 176, 118, 78, 52, 50, 72]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I5-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 57, 63, 91, 147, 203, 236, 313, 364, 378, 379, 371 and 378, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [385, 361, 362, 338, 312, 295, 276, 167, 108, 76, 63, 58]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 21, 33, 28, 60, 96, 120, 118, 133, 135, 146, 149 and 151, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [145, 130, 126, 125, 81, 67, 51, 36, 23, 23, 18, 15]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 21, 38, 53, 120, 209, 259, 287, 319, 360, 392, 427 and 476, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [491, 446, 428, 360, 262, 222, 198, 145, 100, 50, 26, 18]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 28, 44, 101, 146, 178, 206, 228, 246, 256, 271, 262 and 269, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [267, 256, 228, 179, 140, 104, 72, 62, 46, 27, 22, 18]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I15-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 64, 118, 327, 466, 454, 412, 425, 406, 441, 463, 469 and 492, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [498, 456, 430, 368, 288, 224, 189, 134, 96, 62, 44, 37]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I15-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 68, 73, 137, 250, 375, 455, 486, 516, 572, 571, 598 and 658, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [669, 652, 615, 502, 387, 315, 256, 189, 131, 276, 204, 183]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the SR52-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 11, 21, 48, 96, 127, 138, 152, 170, 194, 206, 225 and 262, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [297, 269, 240, 178, 133, 103, 86, 58, 43, 24, 14, 10]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 94, 186, 414, 538, 494, 448, 466, 457, 446, 418, 415 and 404, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [393, 358, 358, 319, 257, 211, 180, 133, 104, 67, 50, 48]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 30, 34, 80, 175, 282, 301, 329, 392, 443, 500, 556 and 694, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [722, 648, 603, 497, 415, 347, 312, 243, 177, 96, 50, 39]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 30, 38, 86, 202, 294, 304, 337, 390, 422, 457, 474 and 550, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [539, 485, 472, 375, 313, 254, 220, 170, 125, 72, 40, 31]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 5, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 122, 296, 552, 678, 650, 618, 648, 660, 664, 654, 680 and 685, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [696, 702, 692, 644, 570, 512, 449, 339, 226, 134, 82, 75]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 137, 220, 404, 481, 506, 514, 514, 534, 524, 517, 528 and 526, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [567, 573, 556, 505, 438, 394, 352, 264, 199, 134, 105, 94]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR55-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 27, 62, 174, 310, 437, 446, 437, 484, 500, 509, 497 and 406, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [360, 376, 377, 410, 386, 341, 301, 237, 139, 63, 45, 30]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR57-S freeway, lane 6, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 86, 230, 478, 512, 546, 578, 588, 591, 606, 622, 651 and 636, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [649, 612, 600, 554, 445, 385, 326, 242, 151, 84, 62, 59]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 6, 11, 29, 51, 105, 96, 101, 102, 103, 104, 109 and 122, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [143, 125, 112, 76, 52, 41, 22, 14, 10, 16, 14, 14]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 2, 5, 14, 35, 66, 88, 92, 94, 114, 111, 128 and 138, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [171, 224, 230, 144, 77, 55, 41, 25, 11, 5, 4, 2]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 3, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 2, 5, 16, 39, 66, 81, 81, 88, 106, 103, 110 and 122, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [150, 195, 196, 120, 72, 49, 39, 24, 12, 7, 4, 3]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 155, 460, 514, 431, 461, 478, 525, 495, 472, 474, 502 and 523, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [484, 483, 463, 452, 381, 330, 312, 222, 142, 89, 68, 82]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I405-S freeway, lane 5, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 2 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 101, 172, 296, 483, 517, 466, 437, 430, 470, 465, 488 and 530, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 PM to 2 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [512, 522, 520, 458, 412, 362, 321, 227, 159, 116, 96, 84]}" } ]