Review of Laboratory Experiments on Travel Choice Behavior
SUN Xiaoyan1,2, HAN Xiao1,3, YAN Xiaoyong3, WANG Wenxu1, JIANG Rui3, JIA Bin3
1.School of Systems Science, Beijing Normal University, Beijing 100875, China; 2.College of Physics and Electronic Engineering, Guangxi Teachers Education University, Nanjing 530023, China; 3.Institute of Transportation Systems Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
Abstract：A fundamental problem in transportation science is to understand the choices of travelers in complex transportation systems. In order to solve these problems, more and more laboratory experiments have been conducted. This paper reviews the main progress of laboratory experiments on travel choice behavior from three aspects, which are traffic network equilibrium test, classic traffic paradoxes test, and travel demand management schemes evaluation. Moreover, this paper analyzes the existing problems and suggests further developments.
孙晓燕, 韩晓, 闫小勇, 王文旭, 姜锐, 贾斌. 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 Xiaoyan, HAN Xiao, YAN Xiaoyong, WANG Wenxu, JIANG Rui, JIA Bin. Review of Laboratory Experiments on Travel Choice Behavior. Complex Systems and Complexity Science, 2017, 14(3): 1-7.
[1]Mahmassani H S. Learning From Interactive Experiments: Travel Behavior and Complex System Dynamics [M]. Bingley, United Kingdom: Emerald Publishing, 2009: 131158.
[2]Rosenthal R W. A class of games possessing pure-strategy nash equilibria [J]. International Journal of Game Theory, 1973, 2: 6567.
[3]Wardrop J G. Some theoretical aspects of road traffic research [J]. Inst Civil Engineer Proc London, 1952, 1: 325378.
[4]Vickrey W S. Congestion theory and transport investment [J]. The American Economic Review, 1969, 59: 251260.
[5]Arnott R, de Palma A, Lindsey R. Economics of a bottleneck [J]. Journal of Urban Economics, 1990, 27: 111130.
[6]Arnott R, de Palma A, Lindsey R. Properties of dynamic traffic equilibrium involving bottlenecks, including a paradox and metering [J]. Transportation Science, 1993, 27: 148160.
[7]Mahmassani H S, Chang G-L, Herman R. Individual decisions and collective effects in a simulated traffic system [J]. Transportation Science, 1986, 20: 258271.
[8]Mahmassani H S, Tong C C. Availability of information and dynamics of departure time choice: experimental investigation [J]. Transportation Research Record, 1988, 1085: 3347.
[9]Mahmassani H S, Herman R. Interactive experiments for the study of tripmaker behaviour dynamics in congested commuting systems [J]. Developments in Dynamic and Activity-Based Approaches to Travel Analysis, 1990, 272279.
[10] Schneider K, Weimann J. Against all odds: nash equilibria in a road pricing experiment [C]// Schrekenberg M, Selten R. Human Behaviour and Traffic Network, Springer Verlag, 2004: 133153.
[11] Ramadurai G, Ukkusuri S V. Dynamic traffic equilibrium theoretical and experimental network game results in single-bottleneck model [J]. Transportation Research Record, 2007, 2029: 113.
[12] Ziegelmeyer A, Koessler F, My K B, et al. Road traffic congestion and public information: an experimental investigation [J]. Journal of Transport Economics and Policy, 2008 42: 4382.
[13] Daniel T E, Gisches E J, Rapoport A, Departure times in Y-shaped traffic networks with multiple bottlenecks[J]. The American Economic Review, 2009, 99:21492176.
[14] 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: 345363.
[15] Rapoport A, Stein W E, Mak V, et al. Endogenous arrivals in batch queues with constant or variable capacity[J]. Transportation Research Part B, 2010, 44: 11661185.
[16] Iida Y, Akiyama T, Uchida T. Experimental analysis of dynamic route choice behavior [J]. Transportation Research Part B, 1992, 26: 1732.
[17] Helbing D, Schönhof M, Kern D. Volatile decision dynamics: experiments, stochastic description, intermittency control and traffic optimization [J]. New Journal of Physics, 2002, 4: 33.133.16.
[18] Selten R, Chmura T, Pitz T, et al. Commuters route choice behaviour [J]. Games and Economic Behavior, 2007, 58: 394406.
[19] Lu X, Gao S, Ben-Elia E. Information impacts on route choice and learning behavior in a congested network: an experimental approach [J]. Transportation Research Record, 2011, 22: 8998.
[20] Lu X, Gao S, Ben-Elia E, et al. Travelers’ day-to-day route choice behavior with real-time information in a congested risky network [J]. Mathematical Population Studies, 2014, 21: 205219.
[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, 2016, 68: 2237.
[22] Dixit V V, Denant-Boemont L. Is equilibrium in transport pure nash, mixed or stochastic? [J] Transportation Research Part C, 2014, 48: 301310.
[23] Ben-Elia E, Avineri E. Response to travel information: a behavioural review [J]. Transport Reviews, 2015, 35: 352377.
[24] 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: 947960.
[25] Rapoport A, Gisches E J, Daniel T, et al. Pre-trip information and route-choice decision with stochastic travel condition: experiment [J]. Transportation Research Part B, 2014, 68: 154172.
[26] Denant-Boèmont L, Petiot R. Information value and sequential decision-making in a transport setting: an experimental study [J]. Transportation Research Part B, 2003, 37: 365386.
[27] Gabuthy Y, Neveu M, Denant-Boemont L. The coordination problem in a structural model of peak-period congestion: an experimental study [J]. Review of Network Economics, 2006, 5: 273298.
[28] 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: 8693.
Liu Tianliang, Zhang Chong, Wang Tiange, et al. Effects of friends' information interaction on travel decisions [J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13: 8693.
[29] Nakayama S, Kitamura R, Fujii S. Drivers’ learning and network behavior: a systematic analysis of the driver-network system as a complex system [J]. Transportation Research Record, 1999, 1493: 3036.
[30] Ye H, Xiao F, Yang H. How people make choices: a day-to-day virtual route choice experiment [C]∥The 7th International Workshop on Behavioral Operations Management, Tianjin, China, 2015.
[31] Avineri E, Prashker J N. Sensitivity to travel time variability: travelers learning perspective [J]. Transportation Research Part C, 2005, 13: 157183.
[32] Helbing D, Schonhof M, Stark H U. How individuals learn to take turns: emergence of alternating cooperation in a congestion game and the prisoner’s dilemma [J]. Advances in Complex Systems, 2005, 8: 87116.
[33] Chorus C G. Risk aversion, regret aversion and travel choice inertia: an experimental study [J]. Transportation Planning and Technology, 2014, 37: 321332.
[34] Chorus C G, Dellaert B. Travel choice inertia: the joint role of risk aversion and learning [J]. Journal of Transport Economics and Policy, 2012, 46: 139155.
[35] Braess D. überein paradoxon aus der verkehrsplanung [J]. Unternehmensforschung, 1968, 12: 258268.
[36] Downs A. The law of peak-hour expressway congestion [J]. Traffic Quarterly, 1962, 16: 393409.
[37] Thomson J M. Great Cities and Their Traffic [M]. London: Peregrine Books, 1978.
[38] Rapoport A, Mak V, Zwick R. Navigating congested networks with variable demand: experimental evidence [J]. Journal of Economic Psychology, 2006, 27: 648666.
[39] 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: 538571.
[40] Rapoport A, Gisches E J, Mak V, et al. Distributed decisions in networks: laboratory study of routing splittable flow [J]. Production and Operations Management, 2014, 23(2): 314331.
[41] Fischbacher U. Z-Tree: Zurich toolbox for ready-made economic experiments [J]. Experimental Economics, 2007, 10: 171178.
[42] Hartman J L. Special issue on transport infrastructure: a route choice experiment with an efficient toll[J]. Networks and Spatial Economics, 2012, 12(2): 205222.
[43] Dechenaux E, Mago S D, Razzolini L. Traffic congestion: an experimental study of the downs-Thomson paradox [J]. Experimental Economics, 2014, 17: 461487.
[44] Morgan J, Orzen H, Sefton M. Network architecture and traffic flows: experiments on the Pigou-Knight-Downs and Braess Paradoxes [J]. Games and Economic Behavior, 2009, 66: 348372.
[45] Anderson L, Holt C A, Reiley D. Congestion pricing and welfare: an entry experiment, environmental economics [J]. Experimental Methods, 2008, 15: 28092.
[46] Yang H, Huang H-J. Mathematical and Economic Theory of Road Pricing [M]. Elsevier Science, 2005.
[47] Yang H, Wang X. Managing network mobility with tradable credits [J]. Transportation Research Part B, 2011, 45: 580594.
[48] Abdul Aziz H M, Ukkusuria S V, Romero J. Understanding short-term travel behavior under personal mobility credit allowance scheme using experimental economics [J]. Transportation Research Part D, 2015, 36: 121137.
[49] Innocenti A, Lattarulo P, Pazienza M G. Car stickiness: heuristics and biases in travel choice [J]. Transport Policy, 2013, 25: 158168.
[50] Dixit V V, Ortmann A, Rutstronn E. Understanding transportation systems through the lenses of experimental economics: a review[DB/OL].[20151230]. http:∥ssm.com/abstract=2546881.
[51] Thøgersen J. Promoting public transport as a subscription service: effects of a free month travel card [J]. Transport Policy, 2009, 16: 335343.
[52] Abou-Zeid M, Ben-Akiva M. Travel mode switching: comparison of findings from two public transportation experiments [J]. Transport Policy, 2012, 24: 4859.
[53] Chidambaram B, Janssen M A, Rommel J, et al. Commuters’ mode choice as a coordination problem: a framed field experiment on traffic policy in Hyderabad, India [J]. Transportation Research Part A, 2014, 65: 922.
[54] Merugu D, Prabhakar B S, Rama N. An incentive mechanism for decongesting the roads: a pilot program in Bangalore [C]. Proc of ACM Net Economics Workshop, 2009.
[55] Rey D, Dixit V V, Ygnace J L, et al. An endogenous lottery-based incentive mechanism to promote off-peak usage in congested transit systems [J]. Transport Policy, 2016, 46: 4655.
[56] 徐红利, 周晶, 徐薇. 基于累积前景理论的随机网络用户均衡模型[J]. 管理科学学报, 2011, 14: 17.
Xu HongLi, Zhou Jing, Xu Wei. Cumulative prospect theory-based user equilibrium model for stochastic network[J]. Journal of management sciences in China, 2011, 14: 17.
[57] Ramos G M, Daamen W, Hoogendoorn S. A state-of-the-art review: developments in utility theory, prospect theory and regret theory to investigate travelers’ behaviour in situations involving travel time uncertainty [J]. Transport Reviews, 2014. 34: 4667.
[58] Xiao L L, Huang H J, Liu R. Congestion behavior and tolls in a bottleneck model with stochastic capacity [J]. Transportation Science, 2015, 49 (1): 4665.
[59] Grant-Muller S, Xu M. The role of tradable credit schemes in road traffic congestion management [J]. Transportation Review, 2014, 34: 128149.
[60] 张小宁, 交通网络拥挤收费原理[M].合肥:合肥工业大学出版社, 2009.
[61] Xiao L, Liu R, Huang H, Congestion behavior under uncertainty on morning commute with preferred arrival time interval [J]. Discrete Dynamics in Nature and Society, 2014, 2014: 19.
