北京市居民地铁出行出发时间弹性时空分布特征研究
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摘要
伴随城市转型进程的加快,交通需求不断膨胀,导致大城市交通拥堵日趋严重,以调节出行者的选择行为为核心要素的交通需求管理理念成为相关政策的重要理论基础,但现有研究也表明,交通需求管理对出行弹性较高的出行具有显著调节作用,而对出行弹性较低的出行调节作用并不明显。因此,加强出行弹性等居民出行行为研究日益迫切,而公交刷卡数据等新的时空数据为居民复杂出行行为的挖掘提供了新的契机。本文利用北京市2014年3月地铁刷卡数据,以出行者出发时刻的可变性来测度出发时间选择的可改变程度,对居民地铁出行出发时间选择弹性进行测度,并结合GIS空间分析技术对其时空分布特征进行分析。研究表明:(1)北京市地铁出行的居民出行弹性平均值为0.521,出发时间选择弹性整体上较大,表明北京居民出发时间选择相对较为灵活;(2)北京市居民地铁出行弹性存在时空差异,居民个体休息日出行弹性高于工作日,一天中高峰时段出行弹性高于非高峰时段;(3)居民出行弹性存在空间自相关,倾向于在空间上发生集聚,存在明显的冷热点区域;内城居民的出行弹性明显高于城市外围居民。
With the acceleration of the urbanization,residents' traffic demand has been continuously increasing,resulting in increasingly severe traffic congestion in large cities.The concept of traffic demand management(TDM)has become an important theoretical basis for relevant policies,but the existing research also shows that TDM has a significant adjustment effect on travel with higher flexibility,while the regulation of travel with lower flexibility is not obvious.Research on mobility behaviors such as travel flexibility has become increasingly urgent,and the new spatio-temporal data,such as smart traffic card data,has provided new opportunities to explore the complex of the residents' travel behavior.Travel elasticity refers to the traveler's preference for the choice of decision variables over a long period of time.It is the selected probability and discreteness of the selection in the travel decision.It is usually used to measure the room for changes in the travel choice behavior,including time elasticity,travel mode flexibility,route flexibility,fare elasticity,etc.In this paper,we measured the travel elasticity of the residents' departure time who takes the subway to work and analyzed the spatial and temporal distribution features based on the smart traffic card data of residents in Beijing in March 2014.The results showed:(1) The average travel elasticity of residents in Beijing who go to work by subway is 0.521.It shows that the overall travel of residents is still relatively flexible,and it also confirms the effectiveness of this research method in revealing the characteristics of residents' travel behavior.(2) There are spatial and temporal differences in the flexibility of Beijing residents.The elasticity of the individual's is higher in the rest days than that of the working day.The elasticity during the peak hours is higher than that in off-peak hours.(3) There are also spatial agglomerations of travel flexibility.Travel elasticity has spatial autocorrelation,tends to agglomerate in space,and there are obvious hot spot areas.At the same time,the inner city residents' travel flexibility is significantly higher than that of the outskirts of the city.
With the acceleration of the urbanization,residents' traffic demand has been continuously increasing,resulting in increasingly severe traffic congestion in large cities.The concept of traffic demand management(TDM)has become an important theoretical basis for relevant policies,but the existing research also shows that TDM has a significant adjustment effect on travel with higher flexibility,while the regulation of travel with lower flexibility is not obvious.Research on mobility behaviors such as travel flexibility has become increasingly urgent,and the new spatio-temporal data,such as smart traffic card data,has provided new opportunities to explore the complex of the residents' travel behavior.Travel elasticity refers to the traveler's preference for the choice of decision variables over a long period of time.It is the selected probability and discreteness of the selection in the travel decision.It is usually used to measure the room for changes in the travel choice behavior,including time elasticity,travel mode flexibility,route flexibility,fare elasticity,etc.In this paper,we measured the travel elasticity of the residents' departure time who takes the subway to work and analyzed the spatial and temporal distribution features based on the smart traffic card data of residents in Beijing in March 2014.The results showed:(1) The average travel elasticity of residents in Beijing who go to work by subway is 0.521.It shows that the overall travel of residents is still relatively flexible,and it also confirms the effectiveness of this research method in revealing the characteristics of residents' travel behavior.(2) There are spatial and temporal differences in the flexibility of Beijing residents.The elasticity of the individual's is higher in the rest days than that of the working day.The elasticity during the peak hours is higher than that in off-peak hours.(3) There are also spatial agglomerations of travel flexibility.Travel elasticity has spatial autocorrelation,tends to agglomerate in space,and there are obvious hot spot areas.At the same time,the inner city residents' travel flexibility is significantly higher than that of the outskirts of the city.
引文
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[5]Thorhauge M,Cherch E,Rich J.How flexible is flexible?Accounting for the effect of rescheduling possibilities in choice of departure time for work trips[J].Transportation Research Part A:Policy and Practice,2016,86:177-193.
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[10]Ewing R.Travel and the built environment:A synthesis[J].Transportation Research Record,2001,1780(1):265-294.
[11]Small K A.The scheduling of consumer activities:Work trips.The American Economic Review,1982,72(3):467-479.
[12]秦萍,陈颖翱,徐晋涛,等.北京居民出行行为分析:时间价值和交通需求弹性估算[J].经济地理,2014,34(11):17-22.[Qin P,Chen YA,Xu J T,et al.Travel behavior analysis for the residents in Beijing:Value of time and travel demand elasticity estimates[J].Economic Geography,2014,34(11):17-22.]
[13]倪亚洲,薛运强,刘彤,等.基于非集计模型的公交票价弹性研究[J].交通标准化,2013(11):66-67.[Ni Y Z,Xue YQ,Liu T,et al.Study of bus fare elasticities based on disaggregate model[J].Transportation Standardization,2013(11):66-67.]
[14]陈旭梅,刘巧仙,杜光.基于SP调查的城市公共交通出行时间价值估计[J].交通运输系统工程与信息,2011,11(4)77-84.[Chen X M,Liu Q X,Du G.Estimation of trave time values for urban public transport passengers based on SP survey[J].Journal of Transportation Systems Engineering and Information Technology,2011,11(4):77-84.]
[15]Thorhauge M,Haustein S,Cherchi E.Accounting for the theory of planned behaviour in departure time choice[J]Transportation Research Part F:Traffic Psychology and Behaviour,2016,38:94-105.
[16]陈梓烽,柴彦威.通勤时空弹性对居民通勤出发时间决策的影响--以北京上地清河地区为例[J].城市发展研究,2014,21(12):65-76.[Chen Z F,Chai Y W.Modelling the choice of departure time for commuting trip chains incorporating space time flexibility variables:Evidence from the Shangdi-Qinghe area of Beijing[J].Urban Development Studies,2014,21(12):65-76.]
[17]倪亚洲,薛运强,刘彤,等.非集计--弹性模型在公交出行需求分析中的应用研究[J].武汉理工大学学报(交通科学与工程版),2014,38(2):442-445.[Ni Y Z,Xue Y QLiu T,et al.Study of disaggregate-elasticity model for bus travel demand analysis[J].Journal of Wuhan University of Technology(Transportation Science&Engineering),2014,38(2):442-445.]
[18]任刚,韩林宁,江航,等.拥堵收费对弹性出行的小汽车使用者出行方式选择影响研究[J].交通信息与安全,201432(3):1-5,21.[Ren G,Han L L,Jiang H,et al.A Study of the impact of congestion pricing on mode choice of automobile users with elastic demand[J].Journal of Transpor Information and Safety,2014,32(3):1-5,21.]
[19]申悦,柴彦威.基于GPS数据的城市居民通勤弹性研究--以北京市郊区巨型社区为例[J].地理学报,2012,67(6):733-744.[Shen Y,Chai Y W.Study on commuting flexibility of residents based on GPS data:A case study of suburban mega-communities in Beijing[J].Acta Geographica Sinica,2012,67(6):733-744.]
[20]李志纯,黄海军.弹性需求下的组合出行模型与求解算法[J].中国公路学报,2005,3:94-98.[Li Z C,Huang H J.Model and solution algorithm with combined travel under elastic demand[J].China Journal of Highway and Transport,2005,3:94-98.]
[21]陆锋,刘康,陈洁.大数据时代的人类移动性研究[J].地球信息科学学报,2014,16(5):665-672.[Lu F,Liu K,Chen J.Research on human mobility in big data Era[J].Journal of Geoinformation Science,2014,16(5):665-672.]
[22]张景秋,贾磊,孟斌.北京城市办公活动空间集聚区研究[J].地理研究,2010,28(4):675-682.[Zhang J Q,Jia L,Meng B.A study on office activities cluster in Beijing city[J].Geographical Research,2010,28(4):675-682.]
[23]赵淑芝,赵贝.多因素影响下的城市居民出行行为时间价值[J].吉林大学学报(工学版),2011,41(1):46-50.[Zhao SZ,Zhao B.Value of travel time of urban resident under multifactor influence[J].Journal of Jilin University(Engineering and Technology Edition),2011,41(1):46-50.]
[24]刘小艳.城市居民出行方式选择行为及影响因素分析[J].交通企业管理,2012,27(6):52-53.[Liu X Y,Urban residents'travel choice behavior and the influencing factors[J].Transportation Enterprise Management,2012,27(6):52-53.]
[25]张伟伟,陈艳艳,赖见辉.轨道交通通勤出行影响因素分析[J].道路交通与安全,2014,14(1):56-59.[Zhang W W,Chen Y Y,Lai J H.Analysis on factors affecting the choices to rail transit for commuters[J].Road Traffic&Safety,2014,14(1):56-59.]
[26]塔娜,柴彦威,关美宝.建成环境对北京市郊区居民工作日汽车出行的影响[J].地理学报,2015,70(10):1675-1685.[Ta N,Chai Y W,Kan Mei-Po.The relationship between the built environment and car travel distance on weekdays in Beijing[J].Acta Geographica Sinica,2015,70(10):1675-1685.]
[27]秦萧,甄峰.大数据与小数据结合:信息时代城市研究方法探讨[J].地理科学,2017,37(3):321-330.[Qin X,Zhen F.Combination between big data and small data:New methods of urban studies in the information Era[J].Scientia Geographica Sinica,2017,37(3):321-330.]
[2]Meyer M D.Demand management as an element of transportation policy:Using carrots and sticks to influence travel behavior[J].Transportation Research Part A:Policy&Practice,1999,33(7-8):575-599.
[3]云美萍,张元,周源,等.出行弹性概念及其应用[J].同济大学学报(自然科学版),2013,41(9):1359-1365.[Yun M P,Zhang Y,Zhou Y,et al.Conception of Travel Elasticity and Its Application[J].Journal of Tongji University(Natural Science),2013,41(9):1359-1365.]
[4]杨励雅,李霞,邵春福,等.居住地、出行方式与出发时间联合选择的交叉巢式Logit模型[J].同济大学学报(自然科学版),2012,40(11):1647-1653.[Yang L Y,Li,X,Shao,C,et al.Cross-nested logit model for joint choice of residential location,travel mode and departure time.Journal of Tongji University(Natural Science),2012,40(11):1647-1653.]
[5]Thorhauge M,Cherch E,Rich J.How flexible is flexible?Accounting for the effect of rescheduling possibilities in choice of departure time for work trips[J].Transportation Research Part A:Policy and Practice,2016,86:177-193.
[6]Hendrickson C,Plank E.The flexibility of departure times for work trips[J].Transportation Research Part A:General,1984,18(1):25-36.
[7]Hess S,Daly A,Rohr C,et al.On the development of time period and mode choice models for use in large scale modelling forecasting systems[J].Transportation Research Part A:Policy and Practice,2007,41(9):802-826.
[8]Marshall A.Principles of economics:An introductory volume[M].Macmillan:Palgrave Macmillan,1920.
[9]Litman T.Transport Elasticities:Impacts on Travel Behaviour[EB/OL].https://www.sutp.org/files/contents/documents/resources/B_Technical-Documents/GIZ_SUTP_TD11_Tra nsport-Elasticities_EN.pdf
[10]Ewing R.Travel and the built environment:A synthesis[J].Transportation Research Record,2001,1780(1):265-294.
[11]Small K A.The scheduling of consumer activities:Work trips.The American Economic Review,1982,72(3):467-479.
[12]秦萍,陈颖翱,徐晋涛,等.北京居民出行行为分析:时间价值和交通需求弹性估算[J].经济地理,2014,34(11):17-22.[Qin P,Chen YA,Xu J T,et al.Travel behavior analysis for the residents in Beijing:Value of time and travel demand elasticity estimates[J].Economic Geography,2014,34(11):17-22.]
[13]倪亚洲,薛运强,刘彤,等.基于非集计模型的公交票价弹性研究[J].交通标准化,2013(11):66-67.[Ni Y Z,Xue YQ,Liu T,et al.Study of bus fare elasticities based on disaggregate model[J].Transportation Standardization,2013(11):66-67.]
[14]陈旭梅,刘巧仙,杜光.基于SP调查的城市公共交通出行时间价值估计[J].交通运输系统工程与信息,2011,11(4)77-84.[Chen X M,Liu Q X,Du G.Estimation of trave time values for urban public transport passengers based on SP survey[J].Journal of Transportation Systems Engineering and Information Technology,2011,11(4):77-84.]
[15]Thorhauge M,Haustein S,Cherchi E.Accounting for the theory of planned behaviour in departure time choice[J]Transportation Research Part F:Traffic Psychology and Behaviour,2016,38:94-105.
[16]陈梓烽,柴彦威.通勤时空弹性对居民通勤出发时间决策的影响--以北京上地清河地区为例[J].城市发展研究,2014,21(12):65-76.[Chen Z F,Chai Y W.Modelling the choice of departure time for commuting trip chains incorporating space time flexibility variables:Evidence from the Shangdi-Qinghe area of Beijing[J].Urban Development Studies,2014,21(12):65-76.]
[17]倪亚洲,薛运强,刘彤,等.非集计--弹性模型在公交出行需求分析中的应用研究[J].武汉理工大学学报(交通科学与工程版),2014,38(2):442-445.[Ni Y Z,Xue Y QLiu T,et al.Study of disaggregate-elasticity model for bus travel demand analysis[J].Journal of Wuhan University of Technology(Transportation Science&Engineering),2014,38(2):442-445.]
[18]任刚,韩林宁,江航,等.拥堵收费对弹性出行的小汽车使用者出行方式选择影响研究[J].交通信息与安全,201432(3):1-5,21.[Ren G,Han L L,Jiang H,et al.A Study of the impact of congestion pricing on mode choice of automobile users with elastic demand[J].Journal of Transpor Information and Safety,2014,32(3):1-5,21.]
[19]申悦,柴彦威.基于GPS数据的城市居民通勤弹性研究--以北京市郊区巨型社区为例[J].地理学报,2012,67(6):733-744.[Shen Y,Chai Y W.Study on commuting flexibility of residents based on GPS data:A case study of suburban mega-communities in Beijing[J].Acta Geographica Sinica,2012,67(6):733-744.]
[20]李志纯,黄海军.弹性需求下的组合出行模型与求解算法[J].中国公路学报,2005,3:94-98.[Li Z C,Huang H J.Model and solution algorithm with combined travel under elastic demand[J].China Journal of Highway and Transport,2005,3:94-98.]
[21]陆锋,刘康,陈洁.大数据时代的人类移动性研究[J].地球信息科学学报,2014,16(5):665-672.[Lu F,Liu K,Chen J.Research on human mobility in big data Era[J].Journal of Geoinformation Science,2014,16(5):665-672.]
[22]张景秋,贾磊,孟斌.北京城市办公活动空间集聚区研究[J].地理研究,2010,28(4):675-682.[Zhang J Q,Jia L,Meng B.A study on office activities cluster in Beijing city[J].Geographical Research,2010,28(4):675-682.]
[23]赵淑芝,赵贝.多因素影响下的城市居民出行行为时间价值[J].吉林大学学报(工学版),2011,41(1):46-50.[Zhao SZ,Zhao B.Value of travel time of urban resident under multifactor influence[J].Journal of Jilin University(Engineering and Technology Edition),2011,41(1):46-50.]
[24]刘小艳.城市居民出行方式选择行为及影响因素分析[J].交通企业管理,2012,27(6):52-53.[Liu X Y,Urban residents'travel choice behavior and the influencing factors[J].Transportation Enterprise Management,2012,27(6):52-53.]
[25]张伟伟,陈艳艳,赖见辉.轨道交通通勤出行影响因素分析[J].道路交通与安全,2014,14(1):56-59.[Zhang W W,Chen Y Y,Lai J H.Analysis on factors affecting the choices to rail transit for commuters[J].Road Traffic&Safety,2014,14(1):56-59.]
[26]塔娜,柴彦威,关美宝.建成环境对北京市郊区居民工作日汽车出行的影响[J].地理学报,2015,70(10):1675-1685.[Ta N,Chai Y W,Kan Mei-Po.The relationship between the built environment and car travel distance on weekdays in Beijing[J].Acta Geographica Sinica,2015,70(10):1675-1685.]
[27]秦萧,甄峰.大数据与小数据结合:信息时代城市研究方法探讨[J].地理科学,2017,37(3):321-330.[Qin X,Zhen F.Combination between big data and small data:New methods of urban studies in the information Era[J].Scientia Geographica Sinica,2017,37(3):321-330.]
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