Day-to-day Traffic Evolution Model under Incident

Abstract

Abstract:

In order to study the temporal and spatial evolution law of day-to-day network flow under shorttime disturbance, network perspective travel time is computed according to past travel experience. With the pursuit goal of maximizing the punctual probability and path prospect, a model is established to adjust schemed departure time and route. On departure day, network travel time is updated again according to the real-time information of each period before departure, and actual departure time and route are readjusted to obtain the actual travel choice. So a model of considering real-time information on departure day is established to describe daily evolution of road network traffic. Finally, a numerical example to verify the model and algorithm, the results show that: in case of no accident, considering adjustment on departure day, flow distribution has smaller amplitude but longer time for stability; in case of accident occurring before stability, the stable flow distribution will be affected, but occurring after stability, the result is opposed; accident will increase flow shock when consider re-adjustment.

Key words: urban traffic, day- to-day network, short- time disturbance, real- time adjustment, punctual probability

摘要：

为研究事故扰动下日变交通路网流量在出发时刻和选择路径上的时空演变规律，以出行经验学习更新路网理解阻抗，基于准点到达概率最大和到达前景最大分别调整计划出发时刻和出行路径.并在出行当日根据出发前各时段的实时信息再次更新路网阻抗，重新调整出发时刻和路径获得实际出行选择.从而建立考虑出行日信息更新的逐日路网流量演化模型.采用算例验证模型，结果表明：在路网无事故情形下，考虑出行日内调整，路网流量变化震荡缓和，但达到稳定所需时间长；事故发生在稳定前，会影响最终平衡态流量分布，而事故发生在稳定后，则不影响；考虑出行日调整会加大路网在事故发生后几日的流量震荡.

关键词: 城市交通, 日变路网, 短时扰动, 实时调整, 准点到达概率

CLC Number:

U268.6

CHEN Ling-juan,WANG Dian-hai. Day-to-day Traffic Evolution Model under Incident[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(2): 112-118.

陈玲娟，王殿海. 短时事故扰动下的逐日路网流量演化模型[J]. 交通运输系统工程与信息, 2017, 17(2): 112-118.

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URL: http://www.tseit.org.cn/EN/abstract/abstract19386.shtml

http://www.tseit.org.cn/EN/Y2017/V17/I2/112

References

[1] NAGURNEY A, ZHANG D. Projected dynamical systems in the formulation, stability analysis, and computation of fixed-demand traffic network equilibria[J]. Transportation Science, 1997, 31(2): 147- 158.

[2] YANG F, LIU H X. A new modeling framework for travelers’day-to-day route choice adjustment processes[C] // Transportation and Traffic Theory 2007. Papers Selected for Presentation at ISTTT17. 2007.

[3] CHANG G L, MAHMASSANI H S. Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system[J]. Transportation Research Part B: Methodological, 1988, 22(3): 217-232.

[4] CASCETTA E. A stochastic process approach to the analysis of temporal dynamics in transportation networks[J]. Transportation Research Part B: Methodological, 1989, 23(1): 1-17.

[5] DAVIS G A, NIHAN N L. Large population approximations of a general stochastic traffic assignment model[J]. Operations Research, 1993, 41(1): 169-178.

[6] HAZELTON M L, WATLING D P. Computation of equilibrium distributions of Markov traffic- assignment models[J]. Transportation Science, 2004, 38(3): 331- 342.

[7] HAZELTON M L, LEE S, POLAK J W. Stationary states in stochastic process models of traffic assignment: A markov chain monte carlo approach[C]//Internaional Symposium on Transportation and Traffic Theory. 1996: 341-357.

[8] SRINIVASAN K K, MAHMASSANI H S. Analyzing heterogeneity and unobserved structural effects in routeswitching behavior under ATIS: A dynamic kernel logit formulation[J]. Transportation Research Part B: Methodological, 2003, 37(9): 793-814.

[9] LIJUN T, HUANG H, TIANLIANG L I U. Day-to-day route choice decision simulation based on dynamic feedback information[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(4): 79-85.

[10] 刘诗序，关宏志．出行者有限理性下的逐日路径选择行为和网络交通流演化[J]. 土木工程学报，2013, 46 (12): 136-144. [LIU S X，GUAN H Z. Travelers’dayto- day route choice behavior and evolution of network traffic flow based on bounded rationality[J]. China Civil Engineering Journal, 2013, 46(12): 136-144.]

[11] HE X, LIU H X. Modeling the day- to- day traffic evolution process after an unexpected network disruption[J]. Transportation Research Part B: Methodological, 2012, 46(1): 50-71.

[12] EBBINGHAUS H. Memory: A contribution to experimental psychology[M]. Boston: University Microfilms, 1913: 63-70.

[13] MILLER G A. The magical number seven, plus or minus two: Some limits on our capacity for processing information[J]. Psychological Review, 1956, 63(2): 81- 97.

[14] SIMON H A. How big is a chunk[J]. Science, 1974, 183 (4124): 482-488.

[15] SMITH M, HAZELTON M L, LO H K, et al. The long term behavior of day-to-day traffic assignment models[J]. Transportation Science, 2014, 10(7): 647- 660.

[16] VICKREY W S. Congestion theory and transport investment[J]. The American Economic Review, 1969, 59(2): 251-260.