天气因素对武汉市出租车出行活动的影响
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摘要
天气状况作为人们生活环境的组成要素之一,对居民日常出行可产生显著的影响,具体可表征为特定空间位置和用地类型范围内出行活动的需求量以及道路交通路线选择的变化。高效、智能化的交通应急管理和城市规划建设亟需理解天气因素影响交通出行时空分布的基本规律。本文选取武汉市作为典型研究区域,基于出租车、气象和空气质量等数据,对不同天气下的居民出行模式和司机路径选择模式进行时空分析,并解释2类模式产生变化的原因和机制。结果表明:(1)从时间上看,工作日的出租车需求量更容易受到天气变化的影响,其中降雨、气温的升高和风速的增强会显著降低居民对出租车的需求;(2)从全市域空间尺度上看,降雨使得居民对出租车的需求量在工作日时段减少,而在周末时段增加,其中降雨主要刺激短距离出租车出行需求而抑制中长距离出行需求;(3)从城郊区空间尺度上看,雨天时段主城区内部的中距离流量减少,郊区内部的短距离流量增加,往返于主城区和郊区的中长距离流量在工作日减少、在周末增加;(4)从功能区空间尺度上看,下雨使得行政办公用地的出租车需求量减少,商业金融用地的出租车需求量在工作日减少、在周末增加,工业用地的出租车需求量在工作日增加、在周末减少;(5)从行驶路径上看,出租车司机在晴天时偏好根据距离来判断最佳路线,而在雨天倾向于改变原先路线选择策略,将距离和车速共同作为最佳路线的指标,选择用时最少的最佳路线。本文研究成果可帮助城市和交通管理部门更加深入地理解城市居民出行规律及其时空分布特征。
Weather conditions have a substantial impact on urban residents' daily travel activities.They usually determine the travel demand within a specific spatial location by land use type,as well as the route selection strategy between a pair of travel origin and destination.This information is crucial for stakeholders including urban dwellers,city planners and transport managers to optimize urban mobility,facility allocation and transportation resilience.In this paper,we apply spatiotemporal statistics,multiple linear regression and clustering analysis on taxi data and weather records of Wuhan City,China to understand the spatiotemporal characteristics of residents' travel demand and taxi drivers' route selection under different weather conditions.As a result,the dominant weather condition factors influencing residents' travel activities are revealed on space and time.First,taxi demand is more vulnerable to weather changes on weekdays than weekends.It is negatively proportional to the increasement of rainfall,temperature and wind speed.Second,at city scale taxi demand decreases along with raining on weekdays while the demand increases on weekends.In particular,the shortdistance travels increase while medium-and long-distance travels decrease.Third,taxi demand is more vulnerable to weather changes within the urban area than the suburban area.On rainy days,medium-distance travels within the urban area decrease,whereas short-distance travels within the suburban area increase.Fourth,taxi demand on residential area increases,whereas the demand on commercial area decreases on rainy days.Last,taxi drivers are found to prefer the shortest path on sunny days and the fastest path on rainy days.Those research results can assist urban planners and municipal managers to enhance their understanding of urban residents' mobility pattern and their spatiotemporal dynamics more deeply.
Weather conditions have a substantial impact on urban residents' daily travel activities.They usually determine the travel demand within a specific spatial location by land use type,as well as the route selection strategy between a pair of travel origin and destination.This information is crucial for stakeholders including urban dwellers,city planners and transport managers to optimize urban mobility,facility allocation and transportation resilience.In this paper,we apply spatiotemporal statistics,multiple linear regression and clustering analysis on taxi data and weather records of Wuhan City,China to understand the spatiotemporal characteristics of residents' travel demand and taxi drivers' route selection under different weather conditions.As a result,the dominant weather condition factors influencing residents' travel activities are revealed on space and time.First,taxi demand is more vulnerable to weather changes on weekdays than weekends.It is negatively proportional to the increasement of rainfall,temperature and wind speed.Second,at city scale taxi demand decreases along with raining on weekdays while the demand increases on weekends.In particular,the shortdistance travels increase while medium-and long-distance travels decrease.Third,taxi demand is more vulnerable to weather changes within the urban area than the suburban area.On rainy days,medium-distance travels within the urban area decrease,whereas short-distance travels within the suburban area increase.Fourth,taxi demand on residential area increases,whereas the demand on commercial area decreases on rainy days.Last,taxi drivers are found to prefer the shortest path on sunny days and the fastest path on rainy days.Those research results can assist urban planners and municipal managers to enhance their understanding of urban residents' mobility pattern and their spatiotemporal dynamics more deeply.
引文
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[19]张凡,韩志玲,陈艳艳,等.降雨天气下公路出行者出行方式转移模型[J].公路交通科技,2018(6):105-111.[Zhang F,Han Z,Chen Y,Li J,Cui W.A model of highway travelers'travel mode shift under rainy weather[J].Journa of Highway and Transportation Research and Development,2018(6):105-111.]
[20]Zhang J,Zheng Y,Qi D.Deep spatio-temporal residua networks for citywide crowd flows prediction[C].Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence,2017:1655-1661.
[21]Analyzing 1.1 billion NYC taxi and Uber trips with a vengeance.Available from http://toddwschneider.com/posts analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/.
[22]杜轶群.雾霾对私家车主交通方式选择行为的影响[J]中国公路学报,2014,27(7):105-110.[Du Y.Impact of haze on travel mode choice behaviors for private car owners[J].China Journal of Highway and Transport,2014,27(7):105-110.]
[23]吴健生,李博,黄秀兰.小城市居民出行行为时空动态及驱动机制研究[J].地球信息科学学报,2017,19(2):176-184.[Wu J,Li B,Huang X.Spatio-temporal dynamics and driving mechanisms of resident trip in small cities[J].Journal of Geo-information Science,2017,19(2):176-184.]
[24]Aaheim H A,Hauge K E.Impacts of climate change on travel habits[J].International Climate and Environmental Research,2005,93(2):227-241.
[25]牟乃夏,张恒才,陈洁,等.轨迹数据挖掘城市应用研究综述[J].地球信息科学学报,2015,17(10):1136-1142.[Mou N X,Zhang H C,Chen J,et al.A review on the application research of trajectory data mining in urban cities[J].Journal of Geo-information Science,2015,17(10):1136-1142.]
[26]Wuhan Land Resources and Planning Bureau.The comprehensive planning of Wuhan[J/OL].http://gtghj.wuhan.gov.cn/pc-0-61109.html.
[27]柳雪丽.特殊气象条件对交通出行安全影响的研究综述[J].智能城市,2017(7):29-31.[Liu X.Review on the influence of special meteorological conditions for traffic security[J].Intelligent City,2017(7):29-31.]
[28]WHO.Review of evidence on health aspects of air pollution-REVIHAAP Project 2013(cited 21 May 2015)[EB/OL].http://www.euro.who.int/en/healthtopics/environment-and-health/air-quality/publications/2013/review-ofevidenceon-health-aspects-of-air-pollution-revihaap-project-final-technical-report.
[29]Yuan H,Zhang Y,Zhou T.A trajectory pattern learning approach based on the normalized edit distance and spectral clustering algorithm[J].Journal of Computer-Aided Design and Computer Graphics,2008,20(6):753-758.
[30]Ward J H.Hierarchical grouping to optimize an objective function[J].Journal of the American Statistical Association,1963,58:236-244.
[2]章锡俏,安实,盛洪飞.不利天气下离散化动态交通路网容量研究[J].哈尔滨工业大学学报,2009,41(7):85-88[Zhang X,An S,Sheng H.Discrete dynamic road network capacity under adverse weather[J].Journal of Harbin Institute of Technology,2009,41(7):85-88.]
[3]龚大鹏,宋国华,黎明,等.降雨对城市道路行程速度的影响[J].交通运输系统工程与信息,2015,15(1):218-225[Gong D,Song G,Li M,et al.Impact of rainfalls on travel speed on urban roads[J].Journal of Transportation Systems Engineering and Information Technology,2015,15(1):218-225.]
[4]Cools M,Moons E,Creemers L,et al.Changes in travel behavior in response to weather conditions:Do type of weather and trip purpose matter?[J].Transportation Research Record:Journal of the Transportation Research Board,2010,2157:22-28.
[5]Keay K,Simmonds I.The association of rainfall and other weather variables with road traffic volume in Melbourne,Australia[J].Accident Analysis&Prevention,2005,37(1):109-124.
[6]Lin L,Ni M,He Q,et al.Modeling the impacts of inclement weather on freeway traffic speed:Exploratory study with social media data[J].Transportation Research Record:Journal of the Transportation Research Board,2015,2482:82-89.
[7]马符铭,刘浩.降雪天气对快速路交通特征的影响及对策[J].交通科技与经济,2016,18(4):53-56.[Ma F,Liu H.The influence of snow weather affects fact road traffic characteristics and its countermeasures[J].Technology&Economy in Areas of Communications,2016,18(4):53-56.]
[8]Theofilatos A,Yannis G.A review of the effect of traffic and weather characteristics on road safety[J].Accident Analysis&Prevention,2014,72:244-256.
[9]敖曼,瞿润平.气象条件对道路交通的影响分析[J].公路与汽运,2011(2):58-62.[Ao M,Qu R.Analysis of the influence of meteorological conditions on road traffic[J].Higways&Automotive Applications,2011(2):58-62.]
[10]Stern E,Zehavi Y.Road safety and hot weather:A study in applied transport geography[J].Transactions of the Institute of British Geographers,1990,15(1):102-111.
[11]Edwards J B.Weather-related road accidents in England and Wales:A spatial analysis[J].Journal of Transport Geography,1996,4(3):201-212.
[12]李聪颖,黄一哲,李敢,等.雾霾天气对出行行为的影响机理研究[J].西安建筑科技大学学报(自然科学版),2015,47(5):728-733.[Li C,Huang Y,Li G,et al.Analysis of the influence of smog on the travel behavior[J].Journal of Xi'an University of Architecture&Technology,2015,47(5):728-733.]
[13]Eisenberg D.The mixed effects of precipitation on traffic crashes[J].Accident Analysis&Prevention,2004,36(4):637-647.
[14]Kamga C,Yazici M A,Singhal A.Hailing in the rain:Temporal and weather-related variations in taxi ridership and taxi demand-supply equilibrium[C].Proceedings of the Transportation Research Board Annual Meeting,2013,13:31-31.
[15]Farber H S.Why you can't find a taxi in the rain and other labor supply lessons from cab drivers[J].The Quarterly Journal of Economics,2015,130(4):1975-2026.
[16]Chung E,Ohtani O,Warita H,et al.Effect of rain on travel demand and traffic accidents[C].Proceedings of the IEEE Intelligent Transportation Systems,2005:1080-1083.
[17]Federal climate complex data documentation for integrated surface data.Available from ftp://ftp.ncdc.noaa.gov pub/data/noaa/ish-format-document.pdf
[18]苏跃江,周芦芦,崔昂.极端天气对城市交通运行的影响分析及对策[J].交通企业管理,2016(10):6-9.[Su YZhou L,Cui A.Analysis of the influence of extreme weather on urban traffic[J].Transportation Enterprise Management,2016(10):6-9.]
[19]张凡,韩志玲,陈艳艳,等.降雨天气下公路出行者出行方式转移模型[J].公路交通科技,2018(6):105-111.[Zhang F,Han Z,Chen Y,Li J,Cui W.A model of highway travelers'travel mode shift under rainy weather[J].Journa of Highway and Transportation Research and Development,2018(6):105-111.]
[20]Zhang J,Zheng Y,Qi D.Deep spatio-temporal residua networks for citywide crowd flows prediction[C].Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence,2017:1655-1661.
[21]Analyzing 1.1 billion NYC taxi and Uber trips with a vengeance.Available from http://toddwschneider.com/posts analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/.
[22]杜轶群.雾霾对私家车主交通方式选择行为的影响[J]中国公路学报,2014,27(7):105-110.[Du Y.Impact of haze on travel mode choice behaviors for private car owners[J].China Journal of Highway and Transport,2014,27(7):105-110.]
[23]吴健生,李博,黄秀兰.小城市居民出行行为时空动态及驱动机制研究[J].地球信息科学学报,2017,19(2):176-184.[Wu J,Li B,Huang X.Spatio-temporal dynamics and driving mechanisms of resident trip in small cities[J].Journal of Geo-information Science,2017,19(2):176-184.]
[24]Aaheim H A,Hauge K E.Impacts of climate change on travel habits[J].International Climate and Environmental Research,2005,93(2):227-241.
[25]牟乃夏,张恒才,陈洁,等.轨迹数据挖掘城市应用研究综述[J].地球信息科学学报,2015,17(10):1136-1142.[Mou N X,Zhang H C,Chen J,et al.A review on the application research of trajectory data mining in urban cities[J].Journal of Geo-information Science,2015,17(10):1136-1142.]
[26]Wuhan Land Resources and Planning Bureau.The comprehensive planning of Wuhan[J/OL].http://gtghj.wuhan.gov.cn/pc-0-61109.html.
[27]柳雪丽.特殊气象条件对交通出行安全影响的研究综述[J].智能城市,2017(7):29-31.[Liu X.Review on the influence of special meteorological conditions for traffic security[J].Intelligent City,2017(7):29-31.]
[28]WHO.Review of evidence on health aspects of air pollution-REVIHAAP Project 2013(cited 21 May 2015)[EB/OL].http://www.euro.who.int/en/healthtopics/environment-and-health/air-quality/publications/2013/review-ofevidenceon-health-aspects-of-air-pollution-revihaap-project-final-technical-report.
[29]Yuan H,Zhang Y,Zhou T.A trajectory pattern learning approach based on the normalized edit distance and spectral clustering algorithm[J].Journal of Computer-Aided Design and Computer Graphics,2008,20(6):753-758.
[30]Ward J H.Hierarchical grouping to optimize an objective function[J].Journal of the American Statistical Association,1963,58:236-244.