城市轨道交通乘客半补偿路径选择建模与应用
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摘要
城轨网络规模的扩大使乘客可选的路径增多,但通常乘客只考虑容忍阈值范围内的路径(即有效路径集合生成),然后从中择优。本文构建的半补偿Mixed Logit模型,基于贝叶斯理论将两个子过程统一,并将偏好系数和阈值参数处理成随机变量以体现乘客的异质性。融合马尔科夫链蒙特卡洛法与数据扩张技术,可内生标定所有参数。基于广州地铁调查数据标定的结果显示,内生标定的出行时间和换乘次数阈值更精准,且所建模型更优。将模型应用于预测广州地铁六号线接入后的换乘量,平均相对误差为5.19%,说明所建模型适应于网络结构变化下的客流预测。
The development of urban rail transit network provides more and more alternative routes to passengers.However,passengers usually only consider the routes(i.e.effective route set generation)in the threshold range of a certain factor(e.g.travel time),and then choose the best one from the set of effective routes.A semi-compensatory Mixed Logit route choice model for urban rail transit passengers was proposed,in which the above two sub-processes were combined based on Bayesian theory and the effective function coefficients and threshold parameters were regarded as random variables to express the differences among passengers' choice preferences and among the thresholds of the effective route set.A model estimation method was designed combining the data augmentation technique and Markov Chain Monte Carlo method to estimate effective function coefficients and threshold parameters endogenously.Based on the surveyed data in Guangzhou Metro,the estimations show that the thresholds are more reliable and the proposed model is better than traditional models.With respect to the scenario of the network structural change caused by the connection of Line 6 with Guangzhou Metro network,the transfer flow volumes of all transfer stations are estimated based on the proposed model,with the mean absolute error of 5.19%.The results demonstrate the adaptability of the proposed model to the travel demand forecasting under the condition of network structural change.
The development of urban rail transit network provides more and more alternative routes to passengers.However,passengers usually only consider the routes(i.e.effective route set generation)in the threshold range of a certain factor(e.g.travel time),and then choose the best one from the set of effective routes.A semi-compensatory Mixed Logit route choice model for urban rail transit passengers was proposed,in which the above two sub-processes were combined based on Bayesian theory and the effective function coefficients and threshold parameters were regarded as random variables to express the differences among passengers' choice preferences and among the thresholds of the effective route set.A model estimation method was designed combining the data augmentation technique and Markov Chain Monte Carlo method to estimate effective function coefficients and threshold parameters endogenously.Based on the surveyed data in Guangzhou Metro,the estimations show that the thresholds are more reliable and the proposed model is better than traditional models.With respect to the scenario of the network structural change caused by the connection of Line 6 with Guangzhou Metro network,the transfer flow volumes of all transfer stations are estimated based on the proposed model,with the mean absolute error of 5.19%.The results demonstrate the adaptability of the proposed model to the travel demand forecasting under the condition of network structural change.
引文
[1]SUN Y,XU R.Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior[J].Transportation Research Record,2012,2275(1):58-67.
[2]ZHOU F,SHI J,XU R.Estimation Method of Path-selecting Proportion for Urban Rail Transit Based on AFC Data[J].Mathematical Problems in Engineering,2015,2015(6):1-9.
[3]SUN L,JIN J,LEE D H,et al.Characterizing Travel Time Reliability and Passenger Path Choice in a Metro Network[C]//Proceedings of Transportation Research Board-94th Annual Meeting.Washington D C:TRB Publisher,2015:1-16.
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[8]徐瑞华,罗钦,高鹏.基于多路径的城市轨道交通网络客流分布模型及算法研究[J].2009,31(2):110-114.XU Ruihua,LUO Qin,GAO Peng.Passenger Flow Distribution Model and Algorithm for Urban Rail Transit Network Based on Multi-route Choice[J].Journal of the China Railway Society,2009,31(2):110-114.
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[10]SUN L,LU Y,JIN J,et al.An Integrated Bayesian Approach for Passenger Flow Assignment in Metro Networks[J].Transportation Research Part C:Emerging Technologies,2015,52(1):116-131.
[11]RAMMING M S.Network Knowledge and Route Choice[D].Massachusetts:Massachusetts Institute of Technology,2001:111-212.
[12]SEBASTIAN R,MUNOZ J C,GRANGE L D.A Topological Route Choice Model for Metro[J].Transportation Research Part A:Policy and Practice,2011,45(2):138-147.
[13]SEBASTIAN R,ZHAN G,JUAN C M,et al.A Behavioral Comparison of Route Choice on Metro Networks:Time,Transfers,Crowding,Topology and Socio-demographics[J].Transportation Research Part A:Policy and Practice,2014,66(1):185-195.
[14]张永生,姚恩建,代洪娜.成网条件下地铁换乘量预测方法研究[J].铁道学报,2013,35(11):1-6.ZHANG Yongsheng,YAO Enjian,DAI Hongna.Transfer Volume Forecasting Method for the Metro in Networking Conditions[J].Journal of the China Railway Society,2013,35(11):1-6.
[15]刘莎莎,姚恩建,张永生.轨道交通乘客个性化出行路径规划算法[J].交通运输系统工程与信息,2014,14(5):100-104.LIU Shasha,YAO Enjian,ZHANG Yongsheng.Personalized Route Planning Algorithm for Urban Rail Transit Passengers[J].Journal of Transportation Systems Engineering and Information Technology,2014,14(5):100-104.
[16]BEN-AKIVA M E,BOCCARA B.Discrete Choice Models with Latent Choice Sets[J].International Journal of Research in Marketing,1995,12(1):9-24.
[17]MCFADDEN D,TRAIN K.Mixed MNL Models for Discrete Response[J].Journal of Applied Econometrics,2000,15(5):447-470.
[18]DAVID A D,MENG X.The Art of Data Augmentation[J].Journal of Computational and Graphical Statistics,2001,10(1):1-50.
[2]ZHOU F,SHI J,XU R.Estimation Method of Path-selecting Proportion for Urban Rail Transit Based on AFC Data[J].Mathematical Problems in Engineering,2015,2015(6):1-9.
[3]SUN L,JIN J,LEE D H,et al.Characterizing Travel Time Reliability and Passenger Path Choice in a Metro Network[C]//Proceedings of Transportation Research Board-94th Annual Meeting.Washington D C:TRB Publisher,2015:1-16.
[4]MCFADDEN D.The Revealed Preferences of Government Bureaucracy[J].The Bell Journal of Economics,1975,6(2):401-416.
[5]YAO E,MORIKAWA T.A Study on Integrated Intercity Travel Demand Model[J].Transportation Research Part A:Policy and Practice,2005,39(4):367-381.
[6]DAAMEN W,BOVV P H L,HOOGENDOOM S P,et al.Passenger Route Choice Concerning Level Changes in Railway Stations[C]//Proceedings of Transportation Research Board-84th Annual Meeting.Washington D C:TRB Publisher,2005:1-18.
[7]四兵锋,毛保华,刘智丽.无缝换乘条件下城市轨道交通网络客流分配模型及算法[J].铁道学报,2007,29(6):12-18.SI Bingfeng,MAO Baohua,LIU Zhili.Passenger Flow Assignment Model for Urban Railway Traffic Network under the Condition of Seamless Exchange[J].Journal of the China Railway Society,2007,29(6):12-18.
[8]徐瑞华,罗钦,高鹏.基于多路径的城市轨道交通网络客流分布模型及算法研究[J].2009,31(2):110-114.XU Ruihua,LUO Qin,GAO Peng.Passenger Flow Distribution Model and Algorithm for Urban Rail Transit Network Based on Multi-route Choice[J].Journal of the China Railway Society,2009,31(2):110-114.
[9]刘剑锋,孙福亮,柏赟,等.城市轨道交通乘客路径选择模型及算法[J].交通运输系统工程与信息,2009,9(2):85-90.LIU Jianfeng,SUN Fuliang,BAI Yun,et al.Passenger Flow Route Assignment Model and Algorithm for Urban Rail Transit Network[J].Journal of Transportation Systems Engineering and Information Technology,2009,9(2):85-90.
[10]SUN L,LU Y,JIN J,et al.An Integrated Bayesian Approach for Passenger Flow Assignment in Metro Networks[J].Transportation Research Part C:Emerging Technologies,2015,52(1):116-131.
[11]RAMMING M S.Network Knowledge and Route Choice[D].Massachusetts:Massachusetts Institute of Technology,2001:111-212.
[12]SEBASTIAN R,MUNOZ J C,GRANGE L D.A Topological Route Choice Model for Metro[J].Transportation Research Part A:Policy and Practice,2011,45(2):138-147.
[13]SEBASTIAN R,ZHAN G,JUAN C M,et al.A Behavioral Comparison of Route Choice on Metro Networks:Time,Transfers,Crowding,Topology and Socio-demographics[J].Transportation Research Part A:Policy and Practice,2014,66(1):185-195.
[14]张永生,姚恩建,代洪娜.成网条件下地铁换乘量预测方法研究[J].铁道学报,2013,35(11):1-6.ZHANG Yongsheng,YAO Enjian,DAI Hongna.Transfer Volume Forecasting Method for the Metro in Networking Conditions[J].Journal of the China Railway Society,2013,35(11):1-6.
[15]刘莎莎,姚恩建,张永生.轨道交通乘客个性化出行路径规划算法[J].交通运输系统工程与信息,2014,14(5):100-104.LIU Shasha,YAO Enjian,ZHANG Yongsheng.Personalized Route Planning Algorithm for Urban Rail Transit Passengers[J].Journal of Transportation Systems Engineering and Information Technology,2014,14(5):100-104.
[16]BEN-AKIVA M E,BOCCARA B.Discrete Choice Models with Latent Choice Sets[J].International Journal of Research in Marketing,1995,12(1):9-24.
[17]MCFADDEN D,TRAIN K.Mixed MNL Models for Discrete Response[J].Journal of Applied Econometrics,2000,15(5):447-470.
[18]DAVID A D,MENG X.The Art of Data Augmentation[J].Journal of Computational and Graphical Statistics,2001,10(1):1-50.