Datasets:

---

TheFuriousGunner

/

Refoematted_Conversations

like 0

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 204, 174, 215, 176, 155, 126, 97, 70, 58, 38, 53 and 50, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [40, 34, 28, 42, 46, 58, 90, 125, 170, 198, 213, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 273, 254, 240, 232, 208, 167, 168, 152, 98, 110, 101 and 69, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [45, 30, 48, 60, 89, 110, 140, 177, 228, 265, 270, 270]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 207, 188, 170, 152, 122, 100, 83, 75, 50, 48, 44 and 27, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [19, 15, 26, 34, 46, 57, 88, 116, 142, 158, 176, 177]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 221, 194, 177, 176, 142, 114, 110, 79, 48, 62, 62 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [27, 38, 36, 47, 50, 84, 139, 212, 246, 274, 284, 248]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 130, 132, 122, 104, 74, 62, 52, 51, 42, 54, 50 and 41, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [29, 30, 30, 33, 40, 62, 98, 128, 152, 154, 159, 150]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 535, 394, 358, 330, 320, 259, 248, 216, 156, 181, 164 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 62, 59, 78, 104, 129, 224, 340, 411, 461, 502, 539]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 273, 252, 236, 244, 232, 192, 193, 175, 120, 134, 141 and 96, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 34, 34, 55, 73, 85, 128, 168, 193, 241, 250, 262]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 334, 312, 312, 296, 260, 235, 198, 166, 134, 135, 146 and 118, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [85, 68, 74, 94, 94, 110, 167, 220, 264, 299, 347, 331]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 271, 256, 242, 218, 176, 147, 135, 126, 107, 113, 112 and 97, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [92, 86, 93, 106, 114, 144, 193, 242, 282, 312, 322, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 349, 327, 298, 275, 242, 222, 196, 167, 113, 123, 125 and 83, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [60, 50, 60, 84, 88, 116, 167, 214, 255, 289, 310, 329]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 142, 128, 116, 101, 82, 62, 62, 57, 36, 52, 52 and 35, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [24, 17, 18, 22, 31, 44, 62, 92, 114, 119, 116, 115]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 92, 82, 81, 67, 55, 46, 33, 22, 16, 17, 25 and 16, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [14, 20, 18, 22, 28, 34, 54, 68, 90, 89, 100, 95]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 333, 350, 357, 340, 269, 228, 204, 179, 105, 77, 114 and 29, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [1, 3, 31, 80, 22, 108, 164, 228, 295, 339, 362, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 298, 280, 262, 256, 213, 190, 162, 121, 74, 42, 25 and 20, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 28, 79, 156, 244, 256, 228, 229, 250, 276, 293, 318]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 230, 252, 240, 238, 163, 122, 94, 72, 54, 64, 74 and 42, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 28, 32, 41, 60, 91, 136, 190, 224, 264, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 471, 475, 470, 498, 464, 426, 411, 339, 220, 147, 177 and 124, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [106, 96, 105, 146, 151, 196, 253, 330, 402, 464, 451, 480]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 302, 284, 278, 235, 208, 177, 176, 169, 144, 221, 259 and 184, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [126, 89, 85, 92, 121, 158, 197, 254, 298, 334, 338, 319]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 220, 213, 201, 193, 164, 148, 130, 97, 62, 35, 18 and 15, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [14, 25, 66, 118, 176, 180, 176, 175, 185, 196, 197, 218]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 286, 286, 280, 247, 210, 196, 172, 154, 137, 113, 120 and 106, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [93, 84, 92, 119, 125, 137, 174, 228, 281, 309, 325, 321]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 396, 395, 368, 356, 310, 269, 230, 208, 145, 120, 134 and 82, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 44, 49, 68, 92, 117, 173, 248, 321, 367, 379, 396]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 397, 373, 349, 342, 285, 253, 216, 160, 99, 56, 34 and 28, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [24, 38, 106, 208, 324, 342, 303, 306, 333, 367, 390, 424]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 208, 211, 202, 196, 167, 146, 126, 106, 90, 124, 132 and 92, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [71, 66, 64, 78, 88, 112, 144, 171, 205, 217, 221, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 585, 567, 526, 505, 448, 390, 336, 300, 292, 276, 353 and 277, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 147, 118, 153, 193, 223, 288, 384, 481, 530, 552, 558]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 326, 334, 314, 292, 254, 205, 168, 134, 63, 94, 131 and 77, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [10, 1, 1, 1, 6, 33, 125, 201, 266, 310, 340, 344]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 292, 291, 287, 263, 224, 195, 156, 134, 100, 124, 134 and 101, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 64, 63, 73, 90, 117, 154, 204, 248, 272, 306, 302]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 430, 473, 464, 460, 383, 309, 247, 212, 154, 204, 221 and 142, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [89, 68, 74, 80, 95, 128, 193, 278, 365, 420, 440, 436]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 346, 355, 352, 336, 283, 221, 194, 173, 122, 150, 213 and 144, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [90, 59, 52, 68, 76, 107, 167, 241, 320, 365, 375, 361]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 224, 221, 209, 157, 105, 80, 67, 49, 36, 55, 39 and 23, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [10, 1, 0, 0, 0, 8, 94, 157, 198, 212, 221, 235]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 166, 152, 153, 129, 107, 77, 59, 44, 32, 36, 37 and 26, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 18, 37, 31, 47, 68, 90, 118, 145, 178, 201, 216]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 254, 309, 281, 228, 200, 194, 181, 147, 87, 98, 104 and 70, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [54, 36, 43, 66, 72, 90, 133, 196, 271, 264, 236, 226]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 121, 131, 136, 119, 115, 100, 100, 76, 42, 52, 57 and 36, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [27, 18, 23, 34, 36, 45, 70, 98, 128, 136, 124, 125]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 502, 504, 501, 464, 412, 372, 322, 253, 163, 98, 64 and 55, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 132, 378, 552, 572, 542, 513, 506, 508, 521, 530, 560]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 402, 405, 400, 369, 323, 288, 245, 191, 124, 74, 46 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 91, 262, 414, 482, 456, 399, 390, 397, 412, 423, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 360, 364, 341, 314, 332, 320, 310, 281, 249, 276, 302 and 280, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [264, 242, 238, 238, 249, 262, 292, 330, 369, 393, 399, 408]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 85, 84, 86, 74, 67, 61, 59, 54, 50, 54, 53 and 49, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [40, 38, 43, 49, 47, 48, 55, 61, 70, 77, 84, 85]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 429, 458, 485, 454, 396, 302, 249, 210, 151, 233, 313 and 205, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 76, 66, 79, 102, 132, 190, 258, 336, 423, 428, 424]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 450, 473, 511, 497, 428, 366, 326, 282, 232, 274, 350 and 265, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [188, 146, 140, 146, 168, 204, 259, 312, 385, 433, 459, 442]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 431, 440, 479, 459, 408, 338, 294, 239, 180, 218, 304 and 208, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 85, 94, 113, 156, 209, 274, 341, 385, 425, 430]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 366, 390, 423, 441, 375, 292, 223, 182, 134, 168, 299 and 188, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [103, 58, 56, 54, 74, 118, 162, 214, 287, 342, 348, 358]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 402, 405, 400, 369, 323, 288, 245, 191, 124, 74, 46 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 91, 262, 414, 482, 456, 399, 390, 397, 412, 423, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 355, 331, 352, 307, 256, 208, 161, 140, 106, 130, 174 and 94, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [72, 41, 43, 62, 88, 118, 175, 263, 356, 403, 417, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 358, 395, 364, 348, 350, 359, 353, 337, 262, 324, 336 and 304, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [230, 159, 111, 108, 111, 139, 192, 256, 347, 438, 406, 386]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 512, 475, 432, 464, 474, 454, 483, 365, 292, 367, 411 and 314, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [230, 176, 210, 221, 231, 253, 304, 365, 417, 469, 492, 513]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 373, 348, 392, 399, 353, 306, 266, 212, 126, 191, 298 and 211, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 82, 116, 147, 144, 150, 193, 270, 319, 340, 349, 349]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 310, 326, 340, 326, 282, 210, 175, 148, 98, 128, 189 and 145, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [91, 86, 161, 192, 188, 185, 189, 237, 258, 260, 277, 288]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 401, 404, 400, 370, 324, 288, 245, 191, 124, 74, 47 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 91, 263, 414, 482, 456, 399, 390, 397, 412, 423, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 564, 544, 560, 516, 466, 419, 332, 274, 187, 201, 248 and 201, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [148, 133, 166, 166, 186, 223, 300, 397, 504, 612, 570, 574]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 540, 532, 553, 503, 464, 422, 350, 307, 226, 236, 269 and 232, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [185, 171, 198, 194, 212, 234, 304, 386, 476, 554, 540, 543]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 292, 326, 316, 281, 273, 230, 223, 200, 141, 190, 220 and 116, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 65, 78, 100, 120, 139, 189, 256, 310, 357, 315, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 534, 551, 619, 538, 538, 463, 420, 400, 332, 352, 468 and 279, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [199, 184, 188, 199, 245, 277, 340, 426, 493, 544, 558, 577]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 605, 612, 712, 610, 586, 494, 434, 424, 340, 362, 512 and 164, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [205, 133, 133, 141, 200, 246, 313, 432, 530, 597, 613, 651]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 378, 372, 407, 386, 356, 302, 252, 235, 176, 206, 280 and 150, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [136, 77, 76, 92, 132, 158, 198, 254, 316, 348, 378, 386]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 471, 485, 498, 456, 408, 339, 289, 243, 186, 215, 234 and 166, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [109, 82, 96, 118, 146, 183, 242, 342, 425, 457, 474, 481]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 608, 662, 670, 570, 542, 480, 456, 412, 300, 392, 488 and 275, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [166, 132, 153, 183, 222, 254, 334, 448, 526, 608, 608, 653]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 647, 636, 634, 579, 532, 457, 416, 325, 210, 127, 83 and 64, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [66, 133, 357, 618, 694, 654, 628, 606, 648, 639, 582, 590]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 433, 456, 490, 453, 421, 349, 307, 258, 186, 231, 342 and 223, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [136, 106, 133, 167, 171, 199, 232, 308, 384, 439, 442, 425]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 324, 342, 354, 338, 290, 231, 202, 166, 123, 147, 226 and 143, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 79, 126, 168, 187, 188, 211, 263, 333, 360, 366, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 271, 270, 273, 278, 266, 263, 227, 207, 170, 154, 189 and 147, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [119, 94, 103, 107, 104, 113, 142, 166, 227, 278, 263, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 401, 405, 400, 369, 323, 288, 245, 191, 124, 74, 47 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 91, 262, 414, 481, 456, 399, 390, 397, 412, 423, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 402, 405, 400, 369, 323, 288, 245, 191, 124, 74, 46 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 91, 262, 414, 482, 456, 399, 390, 397, 412, 423, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 250, 251, 248, 225, 196, 174, 148, 114, 74, 44, 28 and 24, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 57, 168, 277, 325, 302, 254, 244, 246, 253, 258, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 388, 412, 455, 430, 359, 328, 286, 231, 166, 123, 95 and 81, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [122, 344, 416, 418, 423, 422, 430, 434, 448, 459, 470, 501]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 378, 431, 486, 445, 394, 360, 306, 240, 159, 114, 84 and 73, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [140, 355, 460, 519, 509, 494, 486, 486, 500, 511, 526, 557]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 340, 372, 391, 358, 292, 247, 214, 172, 122, 96, 77 and 72, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [121, 267, 347, 459, 462, 404, 386, 392, 411, 423, 448, 471]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 491, 487, 462, 446, 419, 398, 344, 260, 169, 101, 69 and 60, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [79, 165, 344, 433, 476, 500, 468, 469, 478, 491, 506, 551]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 239, 375, 462, 446, 419, 398, 344, 260, 169, 101, 69 and 60, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [79, 165, 344, 433, 476, 500, 468, 469, 478, 491, 506, 551]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 491, 487, 462, 446, 419, 398, 344, 260, 169, 101, 69 and 60, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [79, 165, 344, 433, 476, 500, 468, 469, 478, 491, 506, 551]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 384, 420, 427, 426, 378, 347, 305, 244, 162, 106, 73 and 60, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [59, 94, 160, 232, 251, 244, 266, 300, 332, 389, 435, 488]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 288, 300, 313, 303, 276, 256, 215, 173, 118, 77, 56 and 45, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 69, 126, 167, 186, 191, 206, 236, 266, 286, 327, 350]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 252, 249, 215, 230, 210, 197, 177, 143, 104, 70, 46 and 40, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 58, 99, 162, 178, 190, 198, 218, 237, 251, 253, 255]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 217, 250, 253, 231, 205, 176, 157, 124, 81, 49, 32 and 26, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [31, 71, 127, 160, 208, 237, 212, 213, 230, 246, 270, 279]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 302, 313, 313, 321, 294, 258, 227, 180, 122, 84, 63 and 56, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [105, 222, 213, 284, 381, 371, 342, 357, 360, 363, 378, 395]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 253, 304, 364, 331, 272, 242, 215, 168, 117, 101, 78 and 69, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [116, 311, 282, 344, 363, 318, 315, 298, 320, 320, 341, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 258, 283, 281, 258, 236, 215, 196, 150, 98, 62, 41 and 36, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 88, 164, 288, 345, 306, 249, 255, 276, 286, 303, 335]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 369, 376, 392, 376, 326, 287, 256, 212, 163, 116, 82 and 65, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [71, 115, 181, 263, 338, 348, 320, 338, 366, 399, 424, 492]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 266, 322, 346, 335, 314, 299, 259, 195, 127, 76, 52 and 45, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [59, 124, 258, 325, 357, 375, 351, 352, 359, 368, 380, 413]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 246, 244, 231, 223, 210, 199, 172, 130, 84, 51, 34 and 30, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [40, 82, 172, 216, 238, 250, 234, 234, 239, 246, 254, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 169, 162, 143, 104, 84, 60, 53, 38, 24, 14, 9 and 6, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [8, 9, 9, 11, 24, 44, 66, 91, 121, 154, 177, 211]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 104, 100, 96, 91, 66, 59, 52, 46, 38, 37, 43 and 32, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [26, 26, 29, 34, 33, 41, 50, 66, 76, 92, 98, 106]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 156, 140, 140, 122, 98, 70, 58, 44, 36, 34, 36 and 22, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [16, 14, 20, 27, 37, 52, 70, 97, 136, 143, 167, 185]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 302, 305, 300, 253, 216, 153, 112, 80, 58, 82, 84 and 60, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 42, 55, 51, 73, 102, 136, 179, 230, 286, 308, 318]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 357, 347, 308, 310, 240, 191, 141, 107, 74, 46, 94 and 90, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [70, 61, 60, 70, 64, 100, 164, 236, 326, 370, 380, 402]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 126, 128, 154, 96, 81, 61, 53, 48, 34, 47, 38 and 34, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 32, 24, 27, 27, 38, 55, 79, 99, 116, 121, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 328, 320, 311, 278, 240, 190, 165, 144, 116, 124, 112 and 90, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [59, 43, 34, 50, 68, 87, 131, 170, 214, 250, 291, 307]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 212, 211, 198, 155, 120, 97, 80, 66, 57, 80, 80 and 51, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 24, 33, 38, 51, 78, 113, 154, 203, 243, 256, 266]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 326, 333, 327, 284, 225, 181, 156, 124, 88, 124, 132 and 93, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [60, 48, 61, 81, 108, 147, 189, 234, 291, 305, 330, 322]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 388, 376, 386, 357, 308, 261, 224, 182, 121, 123, 142 and 94, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 48, 66, 71, 94, 118, 169, 222, 292, 327, 374, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 134, 129, 131, 115, 94, 72, 67, 58, 44, 44, 42 and 27, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 20, 36, 41, 61, 87, 110, 126, 132, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 289, 274, 264, 233, 187, 152, 141, 108, 81, 107, 107 and 70, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 48, 73, 96, 128, 171, 220, 269, 311, 327, 327, 319]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 410, 380, 391, 368, 346, 272, 234, 188, 144, 127, 118 and 92, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [65, 43, 35, 57, 96, 105, 145, 192, 244, 306, 342, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 312, 278, 292, 289, 244, 190, 169, 134, 104, 83, 78 and 74, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [49, 35, 31, 55, 73, 94, 136, 184, 235, 275, 296, 290]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 614, 609, 553, 521, 471, 413, 374, 325, 225, 353, 436 and 259, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [151, 106, 108, 127, 158, 203, 282, 372, 498, 536, 572, 538]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 466, 468, 471, 448, 432, 365, 323, 276, 210, 230, 309 and 212, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 112, 118, 144, 180, 234, 276, 345, 396, 434, 458, 449]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 428, 423, 418, 393, 308, 284, 240, 217, 162, 201, 208 and 151, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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, 49, 59, 79, 108, 132, 182, 236, 296, 340, 351, 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 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 504, 498, 499, 484, 416, 347, 292, 223, 167, 232, 234 and 152, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [92, 76, 81, 96, 126, 165, 237, 305, 396, 458, 478, 473]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 63, 64, 60, 55, 36, 28, 20, 15, 11, 14, 13 and 6, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [5, 3, 6, 11, 18, 27, 35, 54, 75, 88, 81, 90]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 124, 123, 113, 87, 79, 72, 61, 40, 18, 12, 6 and 4, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [2, 6, 20, 49, 122, 151, 118, 93, 102, 113, 127, 162]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 94, 100, 98, 80, 56, 37, 26, 24, 17, 22, 23 and 13, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [7, 4, 5, 10, 19, 22, 32, 52, 71, 78, 81, 87]}" } ]

[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 468, 492, 483, 446, 408, 351, 293, 261, 206, 278, 354 and 238, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [158, 84, 88, 118, 181, 231, 292, 348, 405, 431, 426, 419]}" } ]

[ { "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: 2 AM, 2018-1-1, Monday, New Year's Day. - Traffic volume data in the past 12 hours were 434, 438, 422, 394, 308, 262, 225, 197, 146, 187, 210 and 157, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 3 AM to 2 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: [116, 104, 118, 135, 152, 172, 218, 267, 321, 358, 380, 404]}" } ]