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{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Yolo, California, USA, along the US50-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 150, 156, 178, 208, 246, 248, 257, 263, 269, 262, 229 and 221, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [214, 152, 127, 100, 58, 38, 25, 22, 18, 27, 75, 201]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 332, 296, 287, 307, 334, 359, 376, 381, 384, 366, 317 and 282, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [242, 194, 164, 111, 76, 46, 30, 18, 24, 64, 188, 390]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 3, direction of eastbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 134, 138, 142, 166, 198, 212, 226, 242, 260, 264, 232 and 191, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [141, 112, 80, 58, 35, 21, 17, 11, 13, 22, 52, 131]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Yolo, California, USA, along the US50-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 220, 222, 262, 304, 304, 326, 315, 326, 286, 284, 248 and 228, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [253, 231, 161, 116, 57, 35, 30, 36, 90, 170, 265, 347]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in El Dorado, California, USA, along the US50-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 128, 138, 156, 158, 167, 167, 165, 162, 177, 163, 158 and 126, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [103, 85, 62, 50, 27, 16, 12, 14, 23, 45, 110, 232]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 314, 330, 387, 577, 581, 581, 616, 646, 576, 488, 420 and 385, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [332, 256, 220, 155, 107, 69, 48, 40, 45, 72, 203, 371]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 207, 205, 231, 279, 300, 320, 332, 342, 328, 314, 307 and 269, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [241, 188, 169, 112, 74, 47, 69, 31, 27, 41, 124, 246]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-S freeway, lane 3, direction of southbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 256, 267, 320, 350, 378, 404, 371, 378, 374, 359, 365 and 344, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [314, 246, 198, 136, 95, 68, 54, 50, 62, 94, 231, 472]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Placer, California, USA, along the I80-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 157, 192, 238, 288, 356, 343, 344, 352, 340, 292, 291 and 282, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [271, 237, 202, 67, 35, 26, 19, 17, 34, 61, 134, 255]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 270, 278, 318, 370, 384, 384, 400, 408, 371, 335, 325 and 296, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [238, 190, 164, 120, 82, 54, 42, 40, 59, 106, 261, 469]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 111, 122, 137, 141, 156, 164, 163, 160, 160, 156, 156 and 151, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [137, 116, 99, 78, 66, 52, 47, 46, 49, 56, 74, 94]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 5.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 107, 94, 106, 112, 114, 119, 117, 112, 112, 116, 106 and 95, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [65, 49, 38, 22, 14, 11, 9, 11, 28, 64, 115, 225]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 228, 293, 337, 391, 361, 400, 425, 416, 378, 394, 377 and 334, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [264, 221, 193, 133, 17, 2, 1, 1, 20, 257, 500, 453]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Solano, California, USA, along the I80-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 244, 256, 228, 229, 250, 276, 293, 318, 294, 274, 288 and 281, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [229, 169, 144, 106, 64, 37, 23, 20, 20, 30, 87, 181]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 166, 169, 202, 235, 259, 268, 258, 274, 272, 283, 293 and 284, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [288, 200, 173, 120, 76, 52, 36, 34, 56, 167, 275, 336]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in San Mateo, California, USA, along the US101-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 8.3\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 384, 410, 456, 495, 496, 539, 532, 530, 518, 518, 491 and 448, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [406, 402, 371, 252, 148, 99, 58, 47, 79, 184, 422, 592]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Marin, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 187, 245, 278, 319, 349, 349, 350, 355, 364, 352, 330 and 285, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [244, 216, 190, 164, 120, 98, 81, 68, 66, 72, 107, 167]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 176, 180, 176, 175, 185, 196, 197, 218, 212, 209, 207 and 204, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [172, 137, 115, 86, 60, 35, 19, 16, 16, 26, 69, 124]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Marin, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 239, 250, 288, 307, 325, 311, 324, 316, 331, 356, 328 and 279, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [216, 177, 158, 120, 91, 75, 68, 66, 78, 125, 225, 280]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in San Mateo, California, USA, along the I280-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 8.3\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 228, 270, 305, 377, 402, 395, 413, 434, 470, 520, 452 and 375, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [318, 285, 255, 194, 130, 86, 53, 31, 24, 48, 116, 278]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I580-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 324, 342, 303, 306, 333, 367, 390, 424, 393, 365, 384 and 375, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [306, 226, 191, 142, 85, 49, 31, 27, 27, 40, 117, 242]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I680-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 182, 195, 213, 229, 237, 240, 244, 246, 247, 234, 228 and 208, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [190, 165, 134, 105, 81, 66, 57, 55, 66, 106, 160, 238]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the I680-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 382, 449, 498, 622, 682, 732, 750, 810, 803, 809, 699 and 607, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [532, 478, 420, 341, 304, 229, 219, 231, 213, 192, 214, 389]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the I680-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 185, 226, 273, 335, 352, 368, 373, 414, 399, 403, 383 and 370, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [292, 247, 205, 161, 96, 48, 29, 24, 33, 83, 196, 263]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the I680-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 337, 350, 404, 455, 456, 466, 470, 478, 492, 484, 433 and 386, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [325, 288, 232, 188, 128, 94, 74, 77, 98, 184, 329, 486]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the I880-N freeway, lane 6, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 298, 313, 380, 448, 514, 561, 572, 601, 597, 584, 573 and 514, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [486, 394, 321, 229, 170, 104, 70, 62, 88, 176, 320, 414]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I880-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 8.1\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 282, 308, 384, 418, 497, 470, 475, 468, 453, 452, 430 and 370, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [310, 262, 216, 168, 112, 69, 40, 40, 61, 186, 390, 439]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 5 in Santa Cruz, California, USA, along the SR17-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 126, 0, 102, 244, 258, 263, 276, 300, 308, 300, 280 and 52, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [0, 0, 0, 10, 26, 15, 8, 10, 15, 54, 164, 239]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 6 in Kern, California, USA, along the SR99-S freeway, lane 3, direction of southbound. \n - Today's weather: Sunny. Temperature is 9.9\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 146, 145, 147, 190, 212, 216, 258, 260, 278, 284, 226 and 190, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [142, 114, 83, 76, 46, 49, 29, 27, 37, 55, 77, 141]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 285, 306, 315, 302, 314, 332, 305, 328, 343, 364, 339 and 284, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [217, 176, 171, 139, 112, 98, 80, 88, 94, 132, 279, 476]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 208, 204, 196, 182, 170, 167, 178, 185, 226, 226, 197 and 156, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [107, 75, 70, 48, 27, 16, 9, 7, 14, 42, 155, 377]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I5-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 572, 542, 513, 506, 508, 521, 530, 560, 575, 572, 568 and 522, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [440, 338, 306, 238, 153, 99, 74, 78, 77, 117, 308, 500]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I10-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 482, 456, 399, 390, 397, 412, 423, 457, 476, 481, 482 and 437, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [358, 267, 238, 185, 117, 77, 58, 53, 56, 82, 204, 367]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 418, 414, 428, 449, 454, 404, 453, 464, 448, 465, 458 and 436, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [386, 304, 270, 228, 195, 174, 160, 162, 194, 329, 413, 403]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR47-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 54, 60, 56, 60, 84, 61, 69, 82, 87, 114, 86 and 70, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [52, 43, 51, 32, 23, 19, 18, 39, 17, 8, 29, 60]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 364, 346, 353, 408, 438, 492, 501, 489, 478, 478, 510 and 446, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [405, 339, 279, 214, 143, 101, 61, 57, 86, 145, 254, 363]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 5, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 450, 472, 450, 487, 505, 541, 560, 573, 564, 556, 588 and 531, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [448, 416, 358, 305, 227, 187, 149, 151, 183, 254, 327, 441]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 434, 452, 453, 476, 469, 504, 537, 544, 460, 488, 480 and 465, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [424, 388, 348, 286, 198, 164, 117, 120, 140, 222, 328, 448]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 379, 364, 351, 366, 392, 411, 459, 480, 488, 508, 495 and 440, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [381, 350, 300, 228, 144, 93, 65, 63, 76, 140, 272, 414]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR91-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 482, 456, 399, 390, 397, 412, 423, 457, 476, 481, 482 and 437, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [358, 267, 238, 185, 117, 77, 52, 53, 56, 82, 204, 367]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 391, 365, 385, 460, 453, 471, 462, 439, 475, 472, 452 and 391, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [327, 289, 253, 183, 110, 67, 36, 28, 40, 86, 224, 378]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 295, 322, 372, 398, 400, 392, 378, 372, 371, 376, 392 and 363, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [346, 344, 342, 282, 200, 137, 89, 75, 90, 172, 396, 488]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I110-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 449, 466, 498, 504, 504, 462, 441, 482, 452, 487, 493 and 476, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [466, 414, 387, 295, 236, 173, 118, 104, 138, 253, 476, 351]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 324, 338, 392, 462, 469, 480, 486, 466, 453, 421, 419 and 406, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [381, 303, 261, 194, 123, 74, 38, 32, 37, 69, 180, 380]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 290, 304, 310, 324, 358, 356, 354, 358, 358, 374, 364 and 333, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [300, 248, 208, 174, 133, 112, 84, 87, 88, 100, 154, 271]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I210-E freeway, lane 5, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 424, 440, 500, 551, 597, 610, 563, 632, 628, 602, 590 and 526, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [473, 372, 311, 225, 164, 114, 72, 72, 79, 104, 188, 346]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 552, 551, 574, 680, 674, 688, 655, 673, 712, 649, 673 and 636, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [551, 485, 424, 311, 200, 126, 80, 72, 114, 323, 716, 593]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 567, 557, 566, 658, 659, 684, 651, 671, 675, 662, 680 and 628, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [537, 488, 426, 330, 233, 160, 119, 118, 152, 319, 717, 716]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 275, 312, 333, 367, 362, 364, 374, 387, 309, 315, 312 and 337, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [322, 309, 266, 230, 161, 101, 52, 34, 44, 103, 257, 365]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 446, 497, 562, 604, 614, 620, 648, 637, 633, 639, 597 and 565, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [550, 506, 446, 393, 306, 232, 146, 132, 126, 168, 298, 403]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 6, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 476, 536, 615, 682, 685, 714, 763, 752, 770, 739, 692 and 669, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [664, 567, 509, 411, 292, 197, 93, 68, 64, 111, 269, 435]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 346, 362, 370, 406, 448, 468, 502, 487, 418, 448, 456 and 427, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [404, 348, 307, 244, 178, 110, 56, 44, 44, 85, 186, 288]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 576, 560, 516, 506, 521, 550, 561, 550, 497, 458, 465 and 457, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [542, 464, 368, 282, 185, 127, 69, 58, 51, 120, 302, 367]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 594, 624, 667, 693, 680, 702, 699, 672, 494, 528, 611 and 637, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [624, 574, 545, 455, 309, 198, 98, 67, 78, 185, 482, 704]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 308, 342, 378, 388, 424, 432, 403, 508, 523, 484, 523 and 439, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [326, 265, 234, 154, 82, 49, 32, 28, 37, 103, 261, 428]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 444, 457, 476, 495, 520, 534, 552, 547, 513, 542, 543 and 488, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [420, 379, 335, 259, 178, 116, 78, 70, 86, 148, 330, 491]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 330, 332, 348, 378, 418, 448, 434, 370, 343, 354, 316 and 285, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [289, 254, 209, 157, 102, 68, 45, 42, 49, 92, 252, 346]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 324, 290, 304, 327, 338, 352, 334, 364, 380, 366, 383 and 322, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [287, 234, 225, 184, 140, 114, 81, 84, 96, 160, 354, 468]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 481, 456, 399, 390, 397, 412, 423, 458, 476, 481, 482 and 437, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [358, 267, 238, 186, 117, 78, 52, 53, 56, 82, 204, 366]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 482, 456, 399, 390, 397, 412, 423, 457, 476, 481, 482 and 437, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [358, 267, 238, 185, 117, 77, 52, 53, 56, 82, 204, 367]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 325, 302, 254, 244, 246, 253, 258, 286, 322, 337, 339 and 287, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [214, 158, 142, 109, 70, 48, 34, 36, 35, 50, 123, 239]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 370, 377, 416, 451, 468, 258, 466, 496, 474, 516, 492 and 438, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [358, 317, 275, 211, 154, 103, 81, 86, 130, 330, 448, 462]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 420, 378, 416, 464, 481, 482, 459, 488, 436, 418, 386 and 365, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [320, 279, 226, 145, 103, 67, 62, 77, 179, 433, 512, 529]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 349, 334, 367, 422, 436, 441, 448, 485, 466, 448, 409 and 363, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [302, 268, 221, 148, 106, 70, 60, 72, 126, 270, 365, 486]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 476, 500, 468, 469, 478, 491, 506, 551, 591, 570, 589 and 500, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [396, 335, 294, 233, 170, 112, 80, 72, 88, 169, 362, 402]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 185, 214, 247, 274, 282, 294, 290, 282, 300, 284, 263 and 212, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [189, 174, 152, 134, 111, 98, 99, 98, 103, 108, 127, 165]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 248, 287, 326, 372, 383, 355, 348, 353, 298, 306, 340 and 368, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [378, 269, 230, 195, 156, 128, 118, 109, 112, 144, 192, 254]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 212, 229, 249, 264, 275, 284, 313, 344, 315, 327, 370 and 335, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [306, 260, 240, 208, 169, 150, 107, 88, 81, 106, 170, 254]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 193, 209, 250, 290, 323, 327, 366, 384, 394, 418, 450 and 352, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [277, 238, 194, 155, 106, 80, 56, 56, 58, 86, 136, 184]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the SR60-E freeway, lane 2, direction of eastbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 150, 176, 208, 244, 250, 244, 242, 254, 273, 214, 240 and 241, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [214, 186, 161, 125, 94, 63, 52, 39, 46, 62, 113, 172]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the SR71-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 206, 213, 209, 230, 246, 251, 290, 278, 284, 284, 319 and 273, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [215, 174, 147, 106, 60, 39, 26, 26, 32, 72, 142, 172]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the SR91-W freeway, lane 3, direction of westbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 332, 316, 328, 350, 360, 364, 374, 386, 361, 365, 358 and 336, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [289, 256, 201, 156, 102, 66, 47, 48, 106, 219, 262, 328]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I210-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 291, 291, 314, 329, 345, 364, 378, 374, 349, 348, 308 and 327, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [274, 238, 202, 161, 116, 68, 58, 63, 112, 312, 320, 372]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 237, 254, 259, 286, 291, 303, 318, 336, 345, 338, 339 and 284, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [227, 190, 152, 119, 73, 49, 32, 25, 42, 81, 190, 308]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 5, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 266, 289, 316, 343, 370, 410, 440, 480, 548, 548, 541 and 429, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [355, 274, 236, 180, 134, 93, 63, 59, 68, 111, 201, 302]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I215-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 273, 260, 266, 278, 294, 279, 276, 285, 284, 275, 300 and 299, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [258, 233, 210, 180, 153, 122, 110, 116, 139, 204, 281, 316]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-S freeway, lane 2, direction of southbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 238, 250, 234, 234, 239, 246, 254, 276, 296, 286, 295 and 250, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [198, 167, 147, 116, 86, 56, 40, 36, 44, 84, 181, 201]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in Merced, California, USA, along the I5-N freeway, lane 2, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 56, 70, 103, 111, 129, 152, 172, 198, 187, 202, 219 and 223, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [230, 225, 154, 138, 113, 76, 54, 49, 48, 50, 42, 43]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in San Joaquin, California, USA, along the I5-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 65, 74, 81, 97, 99, 112, 113, 134, 160, 130, 130 and 115, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [88, 87, 78, 64, 53, 45, 37, 33, 36, 52, 74, 77]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in Merced, California, USA, along the SR99-N freeway, lane 2, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 101, 116, 125, 150, 172, 179, 191, 184, 190, 196, 180 and 151, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [134, 112, 98, 75, 60, 46, 32, 36, 47, 71, 112, 152]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in Stanislaus, California, USA, along the SR99-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 246, 250, 269, 305, 326, 363, 381, 364, 353, 362, 361 and 295, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [258, 211, 176, 118, 78, 52, 50, 72, 137, 195, 300, 429]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I5-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 203, 236, 313, 364, 378, 379, 371, 378, 385, 361, 362 and 338, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [312, 295, 276, 167, 108, 76, 63, 58, 62, 74, 118, 178]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 96, 120, 118, 133, 135, 146, 149, 151, 145, 130, 126 and 125, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [81, 67, 51, 36, 23, 23, 18, 15, 16, 19, 39, 104]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 209, 259, 287, 319, 360, 392, 427, 476, 491, 446, 428 and 360, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [262, 222, 198, 145, 100, 50, 26, 18, 15, 25, 59, 165]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 178, 206, 228, 246, 256, 271, 262, 269, 267, 256, 228 and 179, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [140, 104, 72, 62, 46, 27, 22, 18, 27, 64, 187, 295]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I15-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 454, 412, 425, 406, 441, 463, 469, 492, 498, 456, 430 and 368, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [288, 224, 189, 134, 96, 62, 44, 37, 64, 175, 498, 726]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I15-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 375, 455, 486, 516, 572, 571, 598, 658, 669, 652, 615 and 502, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [387, 315, 256, 189, 131, 276, 204, 183, 238, 397, 474, 369]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the SR52-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 127, 138, 152, 170, 194, 206, 225, 262, 297, 269, 240 and 178, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [133, 103, 86, 58, 43, 24, 14, 10, 12, 24, 64, 128]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 494, 448, 466, 457, 446, 418, 415, 404, 393, 358, 358 and 319, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [257, 211, 180, 133, 104, 67, 50, 48, 98, 244, 650, 764]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 282, 301, 329, 392, 443, 500, 556, 694, 722, 648, 603 and 497, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [415, 347, 312, 243, 177, 96, 50, 39, 30, 42, 111, 253]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 294, 304, 337, 390, 422, 457, 474, 550, 539, 485, 472 and 375, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [313, 254, 220, 170, 125, 72, 40, 31, 29, 50, 128, 280]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 5, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 650, 618, 648, 660, 664, 654, 680, 685, 696, 702, 692 and 644, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [570, 512, 449, 339, 226, 134, 82, 75, 144, 395, 682, 679]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 506, 514, 514, 534, 524, 517, 528, 526, 567, 573, 556 and 505, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [438, 394, 352, 264, 199, 134, 105, 94, 139, 264, 498, 508]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR55-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 437, 446, 437, 484, 500, 509, 497, 406, 360, 376, 377 and 410, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [386, 341, 301, 237, 139, 63, 45, 30, 32, 84, 219, 419]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR57-S freeway, lane 6, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 546, 578, 588, 591, 606, 622, 651, 636, 649, 612, 600 and 554, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [445, 385, 326, 242, 151, 84, 62, 59, 97, 278, 548, 641]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 105, 96, 101, 102, 103, 104, 109, 122, 143, 125, 112 and 76, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [52, 41, 22, 14, 10, 16, 14, 14, 15, 23, 48, 90]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 66, 88, 92, 94, 114, 111, 128, 138, 171, 224, 230 and 144, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [77, 55, 41, 25, 11, 5, 4, 2, 2, 6, 22, 53]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 66, 81, 81, 88, 106, 103, 110, 122, 150, 195, 196 and 120, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [72, 49, 39, 24, 12, 7, 4, 3, 2, 6, 22, 61]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 461, 478, 525, 495, 472, 474, 502, 523, 484, 483, 463 and 452, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [381, 330, 312, 222, 142, 89, 68, 82, 186, 528, 566, 483]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I405-S freeway, lane 5, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 6 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 517, 466, 437, 430, 470, 465, 488, 530, 512, 522, 520 and 458, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 7 PM to 6 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [412, 362, 321, 227, 159, 116, 96, 84, 106, 204, 369, 506]} |
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