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2022, 03, v.20;No.77 141-153
策略性交通出行选择行为研究评述:实验经济学方法的应用
基金项目(Foundation): 国家自然科学基金项目(71801106,72101085);; 湖北省自然科学基金项目(2021CFB287);; 湖北省教育厅哲学社会科学研究基金项目(20Q119);; 华中师范大学青年团队项目(CCNU20TD004)
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DOI: 10.19961/j.cnki.1672-4747.2022.04.003
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摘要:

随着实验经济学与行为经济学的发展,策略性出行选择行为的实验研究成为交通管理与经济学交叉的热点问题之一。首先,本文从研究方法论和学科发展历程的角度,探讨了实验经济学研究方法应用于交通行为研究的优势和适用性。实验室实验方法具有实施成本低、条件可控、可复制性强、结论内部有效性高的优点,可以作为包括实地调查和实证观测等各类实证研究的起点。其次,综述了实验室实验方法融入和推动出行者路径选择、出行方式选择与出发时间选择等行为研究的进展,并从实验设计中的控制变量(环境或外生变量)、操纵变量(自变量)、观测变量(因变量)三个维度出发,归纳了最近五年的实验研究趋势:(1)对环境变量的设定更趋丰富;(2)对交通需求管理政策的影响评估热度不减;(3)更加关注出行方式和出发时间选择。最后,本文对未来研究提出了五点建议:改变实验控制、放松实验假设、考虑组合决策、注重实验和理论之间的“对话”、评估政策干预或“助推”的效果,从而能够不断检验实验结论的稳健性、外部有效性,并推动理论研究与实证研究之间互相启发、互相促进,为交通管理政策的制定提供真实可靠的微观行为基础和有力有效的影响评估方法。

Abstract:

In transportation and communication networks where route choices are decentralized, utility-maximizing players facing strategic uncertainty often strive to avoid congestion. With the development of the experimental economics methodology, laboratory experiments on travel choice decisions/behavior fall at the intersection of behavioral economics, transportation science, computer science,and operations management. This study briefly summarizes the development history of the experimental economics methodology and discusses its applicability in travel behavior research. In addition, this study reviews the latest experimental research of traveler's route, travel mode, and departure time choices; and summarizes the research trend in the last five years. Finally, this study offers five suggestions for future research: manipulating experimental control variables, relaxing experimental assumptions, considering combined decision-making behavior, examining the interactions between experiments and theories, and evaluating the effectiveness of policy“nudge”. They would test the robustness and external effectiveness of experimental conclusions and promote inspiration and promotion between theoretical and empirical research. Researchers should utilize laboratory experiments to probe individual behavior and assess the impacts of traffic management policies on travelers.

参考文献

[1]黄海军,高自友,田琼,等.新型城镇化导向下的城市群综合交通系统管理[J].中国科学基金, 2018, 32(2):214-223.HUANG Hai-jun, GAO Zi-you, TIAN Qiong, et al. The management theory and method of the comprehensive transportation system in city agglomeration under the new urbanization trend.[J]. Bulletin of National Natural Science Foundation of China, 2018, 32(2):214-223.

[2]贺国光. ITS系统工程导论[M].北京:中国铁道出版社,2004.HE Guo-guang. Introduction to ITS systems engineering[M]. Beijing:China Railway Publishing House, 2004.

[3] DIXIT V V, ORTMANN A, RUTSTROM E E, et al. Experimental economics and choice in transportation:incentives and context[J]. Transportation Research Part C:Emerging Technologies, 2017, 77:161-184.

[4] RAPOPORT A, MAK V, DONOHUE K, et al. Strategic interactions in transportation networks[M]. New Jersey:John Wiley&Sons, Ltd, 2018.

[5] TVERSKY A, KAHNEMAN D. Advances in prospect theory:cumulative representation of uncertainty[J]. Journal of Risk and Uncertainty, 1992, 5(4):297-323.

[6]田丽君,杨茜,黄海军,等.基于累积前景理论的出行方式选择模型及实证[J].系统工程理论与实践, 2016, 36(7):1778-1785.TIAN Li-jun, YANG Qian, HUANG Hai-jun. The cumulative prospect theory-based travel mode choice model and its empirical verification[J]. Systems Engineering-Theory&Practice, 2016, 36(7):1778-1785.

[7]李梦,黄海军.基于后悔理论的出行路径选择行为研究[J].管理科学学报, 2017, 20(11):1-9.LI Meng, HUANG Hai-jun. Modeling route choice behavior based on regret theory[J]. Journal of Management Sciences in China, 2017, 20(11):1-9.

[8]王浩,闫小勇.用自由效用模型刻画出行选择行为[J].交通运输工程与信息学报, 2022, 20(1):47-54.WANG Hao, YAN Xiao-yong. Describing traveler choice behavior using the free utility model[J]. Journal of Transportation Engineering and Information, 2022, 20(1):47-54.

[9]潘晓锋,左志.基于潜在类别模型的出行决策机理异质性研究[J].交通运输工程与信息学报, 2022, 20(1):55-62.PAN Xiao-feng, ZUO Zhi. Utilizing a latent class model to explore heterogeneity in travel decision mechanism[J].Journal of Transportation Engineering and Information,2022, 20(1):55-62.

[10]齐航,夏嘉祺,王光超,等.考虑出行者习惯与利他性偏好的自动驾驶网约车使用意向模型[J].交通运输工程与信息学报, 2021, 19(2):1-10.QI Hang, XIA Jia-qi, WANG Guang-chao, et al. A behavioral intention to use model of autonomous vehicle ridehailing incorporating traveler habit and altruistic preference[J]. Journal of Transportation Engineering and Information, 2021, 19(2):1-10.

[11] WANG G C, JIA N, MA S F, et al. A rank-dependent bicriterion equilibrium model for stochastic transportation environment[J]. European Journal of Operational Research, 2014, 235(3):511-529.

[12] WANG G C, MA S F, JIA N. A combined framework for modeling the evolution of traveler route choice under risk[J]. Transportation Research Part C:Emerging Technologies, 2013, 35:156-179.

[13] SMITH V L. Experimental economics:induced value theory[J]. The American Economic Review, 1976, 66(2):274-279.

[14] ZHU W L, MA S F, TIAN J F, et al. Nonlinear relativeproportion-based route adjustment process for day-today traffic dynamics:modeling, equilibrium and stability analysis[J]. Communications in Nonlinear Science and Numerical Simulation, 2016, 40:129-137.

[15] SONG J G, QI H, WANG G C. Learning towards transportation network equilibrium:a model comparison study[J]. IEEE Access, 2019, 7:155737-155747.

[16] RAPOPORT A, GISCHES E J, DANIEL T, et al. Pretrip information and route-choice decisions with stochastic travel conditions:experiment[J]. Transportation Research Part B:Methodological, 2014, 68:154-172.

[17] BEN-ELIA E, AVINERI E. Response to travel information:a behavioural review[J]. Transport Reviews, 2015,35(3):352-377.

[18] AVINERI E, PRASHKER J N. The impact of travel time information on travelers’learning under uncertainty[J].Transportation, 2006, 33(4):393-408.

[19] BEN-ELIA E, SHIFTAN Y. Which road do I take? A learning-based model of route-choice behavior with realtime information[J]. Transportation Research Part A:Policy and Practice, 2010, 44(4):249-264.

[20] SELTEN R, CHMURA T, PITZ T, et al. Commuters route choice behaviour[J]. Games and Economic Behavior, 2007, 58(2):394-406.

[21] ZHAO C L, HUANG H J. Experiment of boundedly rational route choice behavior and the model under satisficing rule[J]. Transportation Research Part C:Emerging Technologies, 2016, 68:22-37.

[22] QI H, MA S F, JIA N, et al. Individual response modes to pre-trip information in congestible networks:laboratory experiment[J]. Transportmetrica A:Transport Science, 2019, 15(2):376-395.

[23] WEI F, JIA N, MA S F. Day-to-day traffic dynamics considering social interaction:from individual route choice behavior to a network flow model[J]. Transportation Research Part B:Methodological, 2016, 94:335-354.

[24]杜宁华.经济学实验的内部有效性和外部有效性——与朱富强先生商榷[J].学术月刊, 2017, 49(8):80-87.DU Ning-hua. The internal validity and external validity of economic experiments—a discussion with Mr. Zhu Fuqiang[J]. Academic Monthly, 2017, 49(8):80-87.

[25]孙晓燕,韩晓,闫小勇,等.交通出行选择行为实验研究进展[J].复杂系统与复杂性科学, 2017, 14(3):1-7.SUN Xiao-yan, HAN Xiao, YAN Xiao-yong, et al. Review of laboratory experiments on travel choice behavior[J]. Complex Systems and Complexity Science, 2017, 14(3):1-7.

[26] THALER R H, SUNSTEIN C R. Nudge:improving decisions about health, wealth, and happiness[M]. New Haven:Yale University Press, 2008.

[27]齐航.动态路径调整的行为实验与理论建模研究[D].天津:天津大学, 2018.QI Hang. Behavioral experiment and theoretical modeling research of dynamic path adjustment[D]. Tianjin:Tianjin University, 2018.

[28] IIDA Y, AKIYAMA T, UCHIDA T. Experimental analysis of dynamic route choice behavior[J]. Transportation Research Part B:Methodological, 1992, 26(1):17-32.

[29] MENEGUZZER C. Contrarians do better:testing participants’response to information in a simulated day-today route choice experiment[J]. Travel Behaviour and Society, 2019, 15:146-156.

[30] MAK V, GISCHES E J, RAPOPORT A. Route vs. segment:an experiment on real-time travel information in congestible networks[J]. Production and Operations Management, 2015, 24(6):947-960.

[31] KLEIN I, BEN-ELIA E. Emergence of cooperative route-choice:a model and experiment of compliance with system-optimal ATIS[J]. Transportation Research Part F:Traffic Psychology and Behaviour, 2018, 59:348-364.

[32] ADLER J L. Investigating the learning effects of route guidance and traffic advisories on route choice behavior[J]. Transportation Research Part C:Emerging Technologies, 2001, 9(1):1-14.

[33] LIU S X, GUO L D, EASA S M, et al. Experimental study of day-to-day route-choice behavior:evaluating effect of ATIS market penetration[J]. Journal of Advanced Transportation, 2020, 2020(3):8393724.

[34] BEN-ELIA E, EREV I, SHIFTAN Y. The combined effect of information and experience on drivers’routechoice behavior[J]. Transportation, 2008, 35(2):165-177.

[35] YU X L, GAO S. Learning routing policies in a disrupted, congestible network with real-time information:an experimental approach[J]. Transportation research,2019, 106(9):205-219.

[36] LINDSEY R, DANIEL T, GISCHES E, et al. Pre-trip information and route-choice decisions with stochastic travel conditions:theory[J]. Transportation Research Part B:Methodological, 2014, 67:187-207.

[37] DIXIT V V, DENANT-BOEMONT L. Is equilibrium in transport pure Nash, mixed or stochastic?[J]. Transportation Research Part C:Emerging Technologies, 2014, 48:301-310.

[38] RAPOPORT A, QI H, MAK V, et al. When a few undermine the whole:a class of social dilemmas in ridesharing[J]. Journal of Economic Behavior&Organization,2019, 166:125-137.

[39] WANG S Y, GUO R Y, HUANG H J. Day-to-day route choice in networks with different sets for choice:experimental results[J]. Transportmetrica B:Transport Dynamics, 2021, 9(1):712-745.

[40] YE H, XIAO F, YANG H. Exploration of day-to-day route choice models by a virtual experiment[J]. Transportation Research Part C:Emerging Technologies, 2018,94:220-235.

[41] QI H, JIA N, MA S F. A day-to-day traffic dynamic model with asymmetric inertia and preferences:a laboratory experimental study. 2017:1-36(2017-10-25)[2022-03-07]. https://papers. ssrn. com/sol3/papers. cfm? abstract_id=3059405.

[42] STEINBERG R, ZANGWILL W I. The prevalence of Braess’paradox[J]. Transportation Science, 1983, 17(3):301-318.

[43] RAPOPORT A, KUGLER T, DUGAR S, et al. Choice of routes in congested traffic networks:experimental tests of the Braess Paradox[J]. Games and Economic Behavior, 2009, 65(2):538-571.

[44] RAPOPORT A, MAK V, ZWICK R. Navigating congested networks with variable demand:experimental evidence[J]. Journal of Economic Psychology, 2006, 27(5):648-666.

[45] RAPOPORT A, KUGLER T, DUGAR S, et al. Braess paradox in the laboratory:experimental study of route choice in traffic networks with asymmetric costs[M]//Decision Modeling and Behavior in Complex and Uncertain Environments. New York:Springer, 2008:309-337.

[46] GISCHES E J, RAPOPORT A. Degrading network capacity may improve performance:private versus public monitoring in the Braess Paradox[J]. Theory and Decision, 2012, 73(2):267-293.

[47] RAPOPORT A, GISCHES E J, MAK V. Distributed decisions in networks:laboratory study of routing splittable flow[J]. Production and Operations Management,2014, 23(2):314-331.

[48] DENANT-BOèMONT L, HAMMISCHE S. DownsThomson paradox in cities and endogenous choice of transit capacity:an experimental study[J]. Journal of Intelligent Transport Systems:Technology, Planning, and Operations, 2010, 14:140-153.

[49] DECHENAUX E, MAGO S D, RAZZOLINI L. Traffic congestion:an experimental study of the Downs-Thomson paradox[J]. Experimental Economics, 2014, 17(3):461-487.

[50] LIU C, MAK V, RAPOPORT A. Cost-sharing in directed networks:experimental study of equilibrium choice and system dynamics[J]. Journal of Operations Management, 2015, 39:31-47.

[51] HAN X, YU Y, JIA B, et al. Coordination behavior in mode choice:laboratory study of equilibrium transformation and selection[J]. Production and Operations Management, 2021, 30(10):3635-3656.

[52] ZHANG Q R, MA S F, TIAN J F, et al. Mode choice between autonomous vehicles and manually-driven vehicles:an experimental study of information and reward[J]. Transportation Research Part A:Policy and Practices, 2022, 157:24-139.

[53] DANIEL T E, GISCHES E J, RAPOPORT A. Departure times in Y-shaped traffic networks with multiple bottlenecks[J]. American Economic Review, 2009, 99(5):2149-2176.

[54]SUN X, HAN X, BAO J Z, et al. Decision dynamics of departure times:experiments and modeling[J]. Physica A:Statistical Mechanics and its Applications, 2017, 483:74-82.

[55] SUN X Y, LI W T, JIANG R, et al. Study on the influence of road capacity and information feedback on urban traffic system equilibrium state[J]. Physica A:Statistical Mechanics and its Applications, 2022, 593:126935.

[56] YANG Y, JIANG R, HAN X, et al. Experimental study and modeling of departure time choice behavior in the bottleneck model with staggered work hours[J]. Travel Behaviour and Society, 2022, 27:79-94.

[57] MAK V, SEALE D A, GISCHES E J, et al. The Braess paradox and coordination failure in directed networks with mixed externalities[J]. Production and Operations Management, 2018, 27(4):717-733.

[58]刘天亮,张冲,王天歌,等.朋友圈信息交互对个体出行决策行为的影响研究[J].交通运输系统工程与信息, 2013, 13(6):86-93.LIU Tian-liang, ZHANG Chong, WANG Tian-ge, et al.Effects of friends’information interaction on travel decisions[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(6):86-93.

[59] STEIN W E, RAPOPORT A, SEALE D A, et al. Batch queues with choice of arrivals:equilibrium analysis and experimental study[J]. Games and Economic Behavior,2007, 59(2):345-363.

[60] MAK V, SEALE D A, GISCHES E J, et al. A network ridesharing experiment with sequential choice of transportation mode[J]. Theory&Decision, 2018, 85(3-4):1-27.

[61]昝雨尧,王翔,俄文娟,等.多源数据融合的城市区域时变停车需求识别方法[J].交通运输工程与信息学报, 2022, 20(2):82-94.ZAN Yu-yao, WANG Xiang, E Wen-juan, et al. Recognition and monitoring of parking in urban region based on multi-source data[J]. Journal of Transportation Engineering and Information:2022, 20(2):82-94.

[62] HAN X, YU Y, GAO Z Y, et al. The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions[J].Transportation Research Part B:Methodological, 2021,152:205-226.

[63] WANG G C, QI H, XU H L, et al. A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles[J].Journal of Management Science and Engineering, 2020,5(4):227-248.

①这种分类与文献[3]的分类方法在本质上是一致的。

①实地实验/田野实验是介于实验室实验和实地观测方法之间的实验方法,区别于完全基于现实交通场景所自然产生数据的实证观测方法。虚拟现实实验,通常是指在驾驶仪仿真器上进行的,或利用虚拟现实设备进行的实验活动,是介于实验室实验和实地实验之间的实验方法。

②内部有效性或者内部效度是指能够正确地将某个观测现象或结果归因到某一种特定的被研究因素上去,而能够最大程度地避免错误地归因到其他的干扰因素或控制变量上面去。外部有效性是指基于有限的样本中得出的研究结论推广到更为一般的总体中去而仍然能够成立。

③图片来源于文献[3]中图3。

基本信息:

DOI:10.19961/j.cnki.1672-4747.2022.04.003

中图分类号:F512;U491

引用信息:

[1]齐航,于跃洋,王光超,等.策略性交通出行选择行为研究评述:实验经济学方法的应用[J],2022,20(03):141-153.DOI:10.19961/j.cnki.1672-4747.2022.04.003.

基金信息:

国家自然科学基金项目(71801106,72101085);; 湖北省自然科学基金项目(2021CFB287);; 湖北省教育厅哲学社会科学研究基金项目(20Q119);; 华中师范大学青年团队项目(CCNU20TD004)

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