Citation: | SUN Chao, YIN Haowei, ZHANG Wei, LI Menghui. Traffic Equilibrium Model of Reliable Network Based on Bounded Rationality[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 83-90. doi: 10.3969/j.issn.0258-2724.20210679 |
To explore the influence of the uncertainties of traffic systems and travelers’ perception differences on route choice behavior, the bi-objective traffic network equilibrium model is proposed by introducing the network reliability and bounded rationality into travelers’ route choice decision process. To solve multiple solutions of bi-objective user equilibrium model, the Bayesian stochastic user equilibrium model considering travel time reliability and bounded rationality is built, where the Bayesian statistics and bi-level program framework are used to estimate the weight coefficients, and the variational inequality is adopted to build the traffic equilibrium model. The iterative algorithm (IA) and the method of successive average (MSA) are used for the Bayesian estimation model of weight coefficient and variational inequality traffic network equilibrium model, respectively. Case studies show that, the root mean square error (RMSE) of the estimated parameter is decreasing with the increasing of disturbances of observed data and input variable; RMSE reaches to 0.05 after running IA for 15 s, and the convergence accuracy of MSA reaches 10−6 within 1 s; the variational inequality equilibrium model explores traveler’s risk preference and bounded rational decision process.
[1] |
刘志伟,刘建荣,邓卫. 考虑潜在类别的市内机动化出行行为模型[J]. 西南交通大学学报,2021,56(1): 131-137.
LIU Zhiwei, LIU Jianrong, DENG Wei. Inclusion of latent class in behavior model of motorized travel in city[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 131-137.
|
[2] |
LIU W, LI X W, ZHANG F N, et al. Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision[J]. Transportation Research Part C: Emerging Technologies, 2017, 85: 711-731. doi: 10.1016/j.trc.2017.10.021
|
[3] |
WU L X, HUANG Z X, WANG Y L, et al. A dynamic evolution model of disequilibrium network traffic flow with quantity regulation of congestion[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 167-179.
|
[4] |
DE MORAES RAMOS G, MAI T E, DAAMEN W, et al. Route choice behaviour and travel information in a congested network: static and dynamic recursive models[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 681-693. doi: 10.1016/j.trc.2020.02.014
|
[5] |
ZHU S J, LEVINSON D. Do people use the shortest path? An empirical test of Wardrop’s first principle[J]. PLoS One, 2015, 10(8): 0134322.1-0134322.22.
|
[6] |
FAYYAZ M, BLIEMER M C J, BECK M J, et al. Stated choices and simulated experiences: differences in the value of travel time and reliability[J]. Transportation Research Part C: Emerging Technologies, 2021, 128: 103145.1-103145.19.
|
[7] |
WATLING D. User equilibrium traffic network assignment with stochastic travel times and late arrival penalty[J]. European Journal of Operational Research, 2006, 175(3): 1539-1556. doi: 10.1016/j.ejor.2005.02.039
|
[8] |
ZHU Z, XIONG C F, CHEN X Q, et al. Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis[J]. Transportation Research Part C: Emerging Technologies, 2018, 93: 446-462. doi: 10.1016/j.trc.2018.06.011
|
[9] |
ZHANG Y F, KHANI A. An algorithm for reliable shortest path problem with travel time correlations[J]. Transportation Research Part B: Methodological, 2019, 121: 92-113. doi: 10.1016/j.trb.2018.12.011
|
[10] |
XU X D, CHEN A, ZHOU Z, et al. A multi-class mean-excess traffic equilibrium model with elastic demand[J]. Journal of Advanced Transportation, 2014, 48(3): 203-222. doi: 10.1002/atr.205
|
[11] |
SUN C, CHENG L, ZHU S L, et al. Multiclass stochastic user equilibrium model with elastic demand[J]. Transportation Research Record: Journal of the Transportation Research Board, 2015, 2497(1): 1-11. doi: 10.3141/2497-01
|
[12] |
DI X, LIU H X. Boundedly rational route choice behavior: a review of models and methodologies[J]. Transportation Research Part B: Methodological, 2016, 85: 142-179. doi: 10.1016/j.trb.2016.01.002
|
[13] |
赵传林,黄海军. 基于满意准则的有限理性用户均衡流量分配性质研究[J]. 系统工程理论与实践,2014,34(12): 3073-3078. doi: 10.12011/1000-6788(2014)12-3073
ZHAO Chuanlin, HUANG Haijun. Properties of boundedly rational user equilibrium under satisficing rule in traffic assignment problem[J]. Systems Engineering—Theory & Practice, 2014, 34(12): 3073-3078. doi: 10.12011/1000-6788(2014)12-3073
|
[14] |
张新洁,关宏志,赵磊,等. 有限理性视野下出行者出行方式选择分层Logit模型研究[J]. 交通运输系统工程与信息,2018,18(6): 110-116. doi: 10.16097/j.cnki.1009-6744.2018.06.016
ZHANG Xinjie, GUAN Hongzhi, ZHAO Lei, et al. Nested logit model on travel mode choice under boundebly rational view[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(6): 110-116. doi: 10.16097/j.cnki.1009-6744.2018.06.016
|
[15] |
GONZÁLEZ RAMÍREZ H, LECLERCQ L, CHIABAUT N, et al. Travel time and bounded rationality in travellers’ route choice behaviour: a computer route choice experiment[J]. Travel Behaviour and Society, 2021, 22: 59-83. doi: 10.1016/j.tbs.2020.06.011
|
[16] |
SUN C, LI M H, CHENG L, et al. Boundedly rational user equilibrium with restricted unused routes[J]. Discrete Dynamics in Nature and Society, 2016, 2016: 9848916.1-9848916.11.
|
[17] |
WANG D, LIAO F X, GAO Z Y, et al. Tolerance-based strategies for extending the column generation algorithm to the bounded rational dynamic user equilibrium problem[J]. Transportation Research Part B: Methodological, 2019, 119: 102-121. doi: 10.1016/j.trb.2018.11.008
|
[18] |
YU H, ZHU S L, YANG J, et al. A Bayesian method for dynamic origin−destination demand estimation synthesizing multiple sources of data[J]. Sensors, 2021, 21(15): 4971.1-4971.20.
|
[19] |
LEBLANC L J. Mathematical programming algorithms for large scale network equilibrium and network design problems[D]. Evanston: Northwestern University, 1973
|