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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 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 196, 208, 204, 174, 215, 176, 155, 126, 97, 70, 58 and 38, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [53, 50, 40, 34, 28, 42, 46, 58, 90, 125, 170, 198]}" } ]

[ { "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 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 294, 273, 273, 254, 240, 232, 208, 167, 168, 152, 98 and 110, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [101, 69, 45, 30, 48, 60, 89, 110, 140, 177, 228, 265]}" } ]

[ { "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 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 202, 200, 207, 188, 170, 152, 122, 100, 83, 75, 50 and 48, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [44, 27, 19, 15, 26, 34, 46, 57, 88, 116, 142, 158]}" } ]

[ { "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 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 239, 236, 221, 194, 177, 176, 142, 114, 110, 79, 48 and 62, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [62, 40, 27, 38, 36, 47, 50, 84, 139, 212, 246, 274]}" } ]

[ { "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 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 136, 130, 130, 132, 122, 104, 74, 62, 52, 51, 42 and 54, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [50, 41, 29, 30, 30, 33, 40, 62, 98, 128, 152, 154]}" } ]

[ { "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 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 520, 582, 535, 394, 358, 330, 320, 259, 248, 216, 156 and 181, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [164, 124, 73, 62, 59, 78, 104, 129, 224, 340, 411, 461]}" } ]

[ { "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 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 276, 280, 273, 252, 236, 244, 232, 192, 193, 175, 120 and 134, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [141, 96, 50, 34, 34, 55, 73, 85, 128, 168, 193, 241]}" } ]

[ { "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 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 316, 323, 334, 312, 312, 296, 260, 235, 198, 166, 134 and 135, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [146, 118, 85, 68, 74, 94, 94, 110, 167, 220, 264, 299]}" } ]

[ { "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 9.3\\u00b0C, and visibility reaches 8.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 297, 289, 271, 256, 242, 218, 176, 147, 135, 126, 107 and 113, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 97, 92, 86, 93, 106, 114, 144, 193, 242, 282, 312]}" } ]

[ { "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 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 357, 356, 349, 327, 298, 275, 242, 222, 196, 167, 113 and 123, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [125, 83, 60, 50, 60, 84, 88, 116, 167, 214, 255, 289]}" } ]

[ { "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 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 136, 136, 142, 128, 116, 101, 82, 62, 62, 57, 36 and 52, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [52, 35, 24, 17, 18, 22, 31, 44, 62, 92, 114, 119]}" } ]

[ { "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 9.1\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 93, 92, 92, 82, 81, 67, 55, 46, 33, 22, 16 and 17, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [25, 16, 14, 20, 18, 22, 28, 34, 54, 68, 90, 89]}" } ]

[ { "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 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 342, 339, 333, 350, 357, 340, 269, 228, 204, 179, 105 and 77, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [114, 29, 1, 3, 31, 80, 22, 108, 164, 228, 295, 339]}" } ]

[ { "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 9.5\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 300, 307, 298, 280, 262, 256, 213, 190, 162, 121, 74 and 42, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [25, 20, 18, 28, 79, 156, 244, 256, 228, 229, 250, 276]}" } ]

[ { "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: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 206, 220, 230, 252, 240, 238, 163, 122, 94, 72, 54 and 64, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [74, 42, 25, 20, 28, 32, 41, 60, 91, 136, 190, 224]}" } ]

[ { "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 11.2\\u00b0C, and visibility reaches 7.6 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 489, 482, 471, 475, 470, 498, 464, 426, 411, 339, 220 and 147, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 124, 106, 96, 105, 146, 151, 196, 253, 330, 402, 464]}" } ]

[ { "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 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 286, 296, 302, 284, 278, 235, 208, 177, 176, 169, 144 and 221, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [259, 184, 126, 89, 85, 92, 121, 158, 197, 254, 298, 334]}" } ]

[ { "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: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 220, 223, 220, 213, 201, 193, 164, 148, 130, 97, 62 and 35, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [18, 15, 14, 25, 66, 118, 176, 180, 176, 175, 185, 196]}" } ]

[ { "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 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 249, 291, 286, 286, 280, 247, 210, 196, 172, 154, 137 and 113, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [120, 106, 93, 84, 92, 119, 125, 137, 174, 228, 281, 309]}" } ]

[ { "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 11.2\\u00b0C, and visibility reaches 7.6 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 379, 385, 396, 395, 368, 356, 310, 269, 230, 208, 145 and 120, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [134, 82, 56, 44, 49, 68, 92, 117, 173, 248, 321, 367]}" } ]

[ { "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 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 400, 409, 397, 373, 349, 342, 285, 253, 216, 160, 99 and 56, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [34, 28, 24, 38, 106, 208, 324, 342, 303, 306, 333, 367]}" } ]

[ { "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: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 211, 217, 208, 211, 202, 196, 167, 146, 126, 106, 90 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [132, 92, 71, 66, 64, 78, 88, 112, 144, 171, 205, 217]}" } ]

[ { "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 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 592, 605, 585, 567, 526, 505, 448, 390, 336, 300, 292 and 276, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 277, 221, 147, 118, 153, 193, 223, 288, 384, 481, 530]}" } ]

[ { "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: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 324, 314, 326, 334, 314, 292, 254, 205, 168, 134, 63 and 94, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [131, 77, 10, 1, 1, 1, 6, 33, 125, 201, 266, 310]}" } ]

[ { "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 9.6\\u00b0C, and visibility reaches 7.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 286, 291, 292, 291, 287, 263, 224, 195, 156, 134, 100 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [134, 101, 82, 64, 63, 73, 90, 117, 154, 204, 248, 272]}" } ]

[ { "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: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 430, 442, 430, 473, 464, 460, 383, 309, 247, 212, 154 and 204, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [221, 142, 89, 68, 74, 80, 95, 128, 193, 278, 365, 420]}" } ]

[ { "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 10.4\\u00b0C, and visibility reaches 5.2 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 353, 381, 346, 355, 352, 336, 283, 221, 194, 173, 122 and 150, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [213, 144, 90, 59, 52, 68, 76, 107, 167, 241, 320, 365]}" } ]

[ { "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: Sunny. Temperature is 11.5\\u00b0C, and visibility reaches 5.0 miles. - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 210, 211, 224, 221, 209, 157, 105, 80, 67, 49, 36 and 55, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [39, 23, 10, 1, 0, 0, 0, 8, 94, 157, 198, 212]}" } ]

[ { "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 10.7\\u00b0C, and visibility reaches 3.1 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 176, 175, 166, 152, 153, 129, 107, 77, 59, 44, 32 and 36, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [37, 26, 23, 18, 37, 31, 47, 68, 90, 118, 145, 178]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 251, 257, 254, 309, 281, 228, 200, 194, 181, 147, 87 and 98, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [104, 70, 54, 36, 43, 66, 72, 90, 133, 196, 271, 264]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 126, 126, 121, 131, 136, 119, 115, 100, 100, 76, 42 and 52, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [57, 36, 27, 18, 23, 34, 36, 45, 70, 98, 128, 136]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 514, 510, 502, 504, 501, 464, 412, 372, 322, 253, 163 and 98, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [64, 55, 62, 132, 378, 552, 572, 542, 513, 506, 508, 521]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 403, 405, 402, 405, 400, 369, 323, 288, 245, 191, 124 and 74, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [46, 40, 44, 91, 262, 414, 482, 456, 399, 390, 397, 412]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 361, 365, 360, 364, 341, 314, 332, 320, 310, 281, 249 and 276, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [302, 280, 264, 242, 238, 238, 249, 262, 292, 330, 369, 393]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 86, 84, 85, 84, 86, 74, 67, 61, 59, 54, 50 and 54, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [53, 49, 40, 38, 43, 49, 47, 48, 55, 61, 70, 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 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 461, 455, 429, 458, 485, 454, 396, 302, 249, 210, 151 and 233, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [313, 205, 118, 76, 66, 79, 102, 132, 190, 258, 336, 423]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 456, 454, 450, 473, 511, 497, 428, 366, 326, 282, 232 and 274, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [350, 265, 188, 146, 140, 146, 168, 204, 259, 312, 385, 433]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 427, 429, 431, 440, 479, 459, 408, 338, 294, 239, 180 and 218, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [304, 208, 132, 93, 85, 94, 113, 156, 209, 274, 341, 385]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 370, 378, 366, 390, 423, 441, 375, 292, 223, 182, 134 and 168, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [299, 188, 103, 58, 56, 54, 74, 118, 162, 214, 287, 342]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 403, 405, 402, 405, 400, 369, 323, 288, 245, 191, 124 and 74, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [46, 40, 44, 91, 262, 414, 482, 456, 399, 390, 397, 412]}" } ]

[ { "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 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 374, 350, 355, 331, 352, 307, 256, 208, 161, 140, 106 and 130, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [174, 94, 72, 41, 43, 62, 88, 118, 175, 263, 356, 403]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 351, 359, 358, 395, 364, 348, 350, 359, 353, 337, 262 and 324, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [336, 304, 230, 159, 111, 108, 111, 139, 192, 256, 347, 438]}" } ]

[ { "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 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 496, 516, 512, 475, 432, 464, 474, 454, 483, 365, 292 and 367, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [411, 314, 230, 176, 210, 221, 231, 253, 304, 365, 417, 469]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 377, 382, 373, 348, 392, 399, 353, 306, 266, 212, 126 and 191, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 211, 114, 82, 116, 147, 144, 150, 193, 270, 319, 340]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 316, 322, 310, 326, 340, 326, 282, 210, 175, 148, 98 and 128, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [189, 145, 91, 86, 161, 192, 188, 185, 189, 237, 258, 260]}" } ]

[ { "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 13.9\\u00b0C, and visibility reaches 6.6 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 403, 405, 401, 404, 400, 370, 324, 288, 245, 191, 124 and 74, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [47, 40, 44, 91, 263, 414, 482, 456, 399, 390, 397, 412]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 559, 559, 564, 544, 560, 516, 466, 419, 332, 274, 187 and 201, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [248, 201, 148, 133, 166, 166, 186, 223, 300, 397, 504, 612]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 543, 545, 540, 532, 553, 503, 464, 422, 350, 307, 226 and 236, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 232, 185, 171, 198, 194, 212, 234, 304, 386, 476, 554]}" } ]

[ { "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 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 297, 305, 292, 326, 316, 281, 273, 230, 223, 200, 141 and 190, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [220, 116, 74, 65, 78, 100, 120, 139, 189, 256, 310, 357]}" } ]

[ { "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 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 515, 533, 534, 551, 619, 538, 538, 463, 420, 400, 332 and 352, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [468, 279, 199, 184, 188, 199, 245, 277, 340, 426, 493, 544]}" } ]

[ { "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 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 597, 594, 605, 612, 712, 610, 586, 494, 434, 424, 340 and 362, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 164, 205, 133, 133, 141, 200, 246, 313, 432, 530, 597]}" } ]

[ { "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 13.7\\u00b0C, and visibility reaches 6.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 375, 362, 378, 372, 407, 386, 356, 302, 252, 235, 176 and 206, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [280, 150, 136, 77, 76, 92, 132, 158, 198, 254, 316, 348]}" } ]

[ { "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 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 480, 489, 471, 485, 498, 456, 408, 339, 289, 243, 186 and 215, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [234, 166, 109, 82, 96, 118, 146, 183, 242, 342, 425, 457]}" } ]

[ { "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 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 582, 612, 608, 662, 670, 570, 542, 480, 456, 412, 300 and 392, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 275, 166, 132, 153, 183, 222, 254, 334, 448, 526, 608]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 663, 658, 647, 636, 634, 579, 532, 457, 416, 325, 210 and 127, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [83, 64, 66, 133, 357, 618, 694, 654, 628, 606, 648, 639]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 426, 439, 433, 456, 490, 453, 421, 349, 307, 258, 186 and 231, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [342, 223, 136, 106, 133, 167, 171, 199, 232, 308, 384, 439]}" } ]

[ { "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 6.6 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 341, 340, 324, 342, 354, 338, 290, 231, 202, 166, 123 and 147, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 143, 94, 79, 126, 168, 187, 188, 211, 263, 333, 360]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 270, 306, 271, 270, 273, 278, 266, 263, 227, 207, 170 and 154, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [189, 147, 119, 94, 103, 107, 104, 113, 142, 166, 227, 278]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 402, 405, 401, 405, 400, 369, 323, 288, 245, 191, 124 and 74, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [47, 40, 44, 91, 262, 414, 481, 456, 399, 390, 397, 412]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 403, 405, 402, 405, 400, 369, 323, 288, 245, 191, 124 and 74, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [46, 40, 44, 91, 262, 414, 482, 456, 399, 390, 397, 412]}" } ]

[ { "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 13.8\\u00b0C, and visibility reaches 3.2 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 252, 252, 250, 251, 248, 225, 196, 174, 148, 114, 74 and 44, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [28, 24, 28, 57, 168, 277, 325, 302, 254, 244, 246, 253]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 381, 388, 388, 412, 455, 430, 359, 328, 286, 231, 166 and 123, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [95, 81, 122, 344, 416, 418, 423, 422, 430, 434, 448, 459]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 379, 388, 378, 431, 486, 445, 394, 360, 306, 240, 159 and 114, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [84, 73, 140, 355, 460, 519, 509, 494, 486, 486, 500, 511]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 372, 356, 340, 372, 391, 358, 292, 247, 214, 172, 122 and 96, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [77, 72, 121, 267, 347, 459, 462, 404, 386, 392, 411, 423]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 506, 492, 491, 487, 462, 446, 419, 398, 344, 260, 169 and 101, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [69, 60, 79, 165, 344, 433, 476, 500, 468, 469, 478, 491]}" } ]

[ { "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: Sunny. Temperature is 10.4\\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: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 249, 246, 239, 375, 462, 446, 419, 398, 344, 260, 169 and 101, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [69, 60, 79, 165, 344, 433, 476, 500, 468, 469, 478, 491]}" } ]

[ { "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: Sunny. Temperature is 10.4\\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: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 506, 492, 491, 487, 462, 446, 419, 398, 344, 260, 169 and 101, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [69, 60, 79, 165, 344, 433, 476, 500, 468, 469, 478, 491]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 360, 383, 384, 420, 427, 426, 378, 347, 305, 244, 162 and 106, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [73, 60, 59, 94, 160, 232, 251, 244, 266, 300, 332, 389]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 293, 282, 288, 300, 313, 303, 276, 256, 215, 173, 118 and 77, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [56, 45, 47, 69, 126, 167, 186, 191, 206, 236, 266, 286]}" } ]

[ { "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: Sunny. Temperature is 10.4\\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: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 270, 258, 252, 249, 215, 230, 210, 197, 177, 143, 104 and 70, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [46, 40, 37, 58, 99, 162, 178, 190, 198, 218, 237, 251]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 228, 234, 217, 250, 253, 231, 205, 176, 157, 124, 81 and 49, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [32, 26, 31, 71, 127, 160, 208, 237, 212, 213, 230, 246]}" } ]

[ { "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: Sunny. Temperature is 10.4\\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: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 332, 314, 302, 313, 313, 321, 294, 258, 227, 180, 122 and 84, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [63, 56, 105, 222, 213, 284, 381, 371, 342, 357, 360, 363]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 252, 249, 253, 304, 364, 331, 272, 242, 215, 168, 117 and 101, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [78, 69, 116, 311, 282, 344, 363, 318, 315, 298, 320, 320]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 241, 257, 258, 283, 281, 258, 236, 215, 196, 150, 98 and 62, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [41, 36, 46, 88, 164, 288, 345, 306, 249, 255, 276, 286]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 346, 367, 369, 376, 392, 376, 326, 287, 256, 212, 163 and 116, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [82, 65, 71, 115, 181, 263, 338, 348, 320, 338, 366, 399]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 271, 269, 266, 322, 346, 335, 314, 299, 259, 195, 127 and 76, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [52, 45, 59, 124, 258, 325, 357, 375, 351, 352, 359, 368]}" } ]

[ { "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: Sunny. Temperature is 13.8\\u00b0C, and visibility reaches 9.3 miles. - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 253, 246, 246, 244, 231, 223, 210, 199, 172, 130, 84 and 51, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [34, 30, 40, 82, 172, 216, 238, 250, 234, 234, 239, 246]}" } ]

[ { "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 10.5\\u00b0C, and visibility reaches 3.6 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 155, 162, 169, 162, 143, 104, 84, 60, 53, 38, 24 and 14, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [9, 6, 8, 9, 9, 11, 24, 44, 66, 91, 121, 154]}" } ]

[ { "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: Fog. Temperature is 9.0\\u00b0C, and visibility reaches 6.4 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 104, 101, 104, 100, 96, 91, 66, 59, 52, 46, 38 and 37, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [43, 32, 26, 26, 29, 34, 33, 41, 50, 66, 76, 92]}" } ]

[ { "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 10.5\\u00b0C, and visibility reaches 3.6 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 144, 148, 156, 140, 140, 122, 98, 70, 58, 44, 36 and 34, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [36, 22, 16, 14, 20, 27, 37, 52, 70, 97, 136, 143]}" } ]

[ { "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: Fog. Temperature is 10.4\\u00b0C, and visibility reaches 5.6 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 311, 309, 302, 305, 300, 253, 216, 153, 112, 80, 58 and 82, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [84, 60, 41, 42, 55, 51, 73, 102, 136, 179, 230, 286]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 378, 368, 357, 347, 308, 310, 240, 191, 141, 107, 74 and 46, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [94, 90, 70, 61, 60, 70, 64, 100, 164, 236, 326, 370]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 126, 126, 126, 128, 154, 96, 81, 61, 53, 48, 34 and 47, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [38, 34, 32, 32, 24, 27, 27, 38, 55, 79, 99, 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 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. - Today's weather: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 299, 320, 328, 320, 311, 278, 240, 190, 165, 144, 116 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 90, 59, 43, 34, 50, 68, 87, 131, 170, 214, 250]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 214, 210, 212, 211, 198, 155, 120, 97, 80, 66, 57 and 80, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [80, 51, 34, 24, 33, 38, 51, 78, 113, 154, 203, 243]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 340, 325, 326, 333, 327, 284, 225, 181, 156, 124, 88 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [132, 93, 60, 48, 61, 81, 108, 147, 189, 234, 291, 305]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 388, 389, 388, 376, 386, 357, 308, 261, 224, 182, 121 and 123, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [142, 94, 64, 48, 66, 71, 94, 118, 169, 222, 292, 327]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 130, 133, 134, 129, 131, 115, 94, 72, 67, 58, 44 and 44, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [42, 27, 18, 15, 14, 20, 36, 41, 61, 87, 110, 126]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 327, 307, 289, 274, 264, 233, 187, 152, 141, 108, 81 and 107, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [107, 70, 50, 48, 73, 96, 128, 171, 220, 269, 311, 327]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 376, 394, 410, 380, 391, 368, 346, 272, 234, 188, 144 and 127, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [118, 92, 65, 43, 35, 57, 96, 105, 145, 192, 244, 306]}" } ]

[ { "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: Fog. Temperature is 13.8\\u00b0C, and visibility reaches 4.5 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 305, 313, 312, 278, 292, 289, 244, 190, 169, 134, 104 and 83, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [78, 74, 49, 35, 31, 55, 73, 94, 136, 184, 235, 275]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 580, 571, 614, 609, 553, 521, 471, 413, 374, 325, 225 and 353, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 259, 151, 106, 108, 127, 158, 203, 282, 372, 498, 536]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.4 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 469, 465, 466, 468, 471, 448, 432, 365, 323, 276, 210 and 230, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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, 212, 146, 112, 118, 144, 180, 234, 276, 345, 396, 434]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 424, 414, 428, 423, 418, 393, 308, 284, 240, 217, 162 and 201, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [208, 151, 83, 49, 59, 79, 108, 132, 182, 236, 296, 340]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 510, 494, 504, 498, 499, 484, 416, 347, 292, 223, 167 and 232, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [234, 152, 92, 76, 81, 96, 126, 165, 237, 305, 396, 458]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 75, 69, 63, 64, 60, 55, 36, 28, 20, 15, 11 and 14, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [13, 6, 5, 3, 6, 11, 18, 27, 35, 54, 75, 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 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 113, 121, 124, 123, 113, 87, 79, 72, 61, 40, 18 and 12, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [6, 4, 2, 6, 20, 49, 122, 151, 118, 93, 102, 113]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 88, 87, 94, 100, 98, 80, 56, 37, 26, 24, 17 and 22, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [23, 13, 7, 4, 5, 10, 19, 22, 32, 52, 71, 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 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. - Today's weather: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 430, 442, 468, 492, 483, 446, 408, 351, 293, 261, 206 and 278, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [354, 238, 158, 84, 88, 118, 181, 231, 292, 348, 405, 431]}" } ]

[ { "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: Fog. Temperature is 13.1\\u00b0C, and visibility reaches 3.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 0 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 402, 427, 434, 438, 422, 394, 308, 262, 225, 197, 146 and 187, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 AM to 12 PM). 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: [210, 157, 116, 104, 118, 135, 152, 172, 218, 267, 321, 358]}" } ]