Citation

Lou X, Cheng L, Li D, Zhu S, Zhou J. Transp. Res. Rec. 2018; 2672(48): 12-23.

Affiliation

Department of Urban and Rural Construction, Zhejiang Development & Planning Institute, Hangzhou, China 2School of Transportation, Southeast University, Nanjing, China 3Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, School of Transportation, Southeast University, Nanjing, China 4Zhejiang Institute of Communications, Hangzhou, China Corresponding Author: Address correspondence to Lin Cheng: gist@seu.edu.cn

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118773565

PMID

unavailable

Abstract

In stochastic transport networks, travelers tend to reserve a "travel time budget" (TTB) for every alternative route to assure their punctual arrival with a predefined confidence level. This paper incorporates the concept of TTB into a deterministic nonlinear dynamic system to model the day-to-day evolution process of travelers' risk-taking route choices in stochastic degradable road networks. This paper also introduces consistent traffic information prediction, which can be provided by advanced traveler information systems (ATIS), into the dynamic system to investigate its influence on travelers' route choice behaviors and the corresponding day-to-day network flow evolutions. In the proposed dynamic model, ATIS-equipped travelers are distinguished from those not so equipped to reflect traveler heterogeneity in access to traffic information. Some theoretical analyses are further conducted to investigate the existence and uniqueness of the fixed point, and the stability of the day-to-day dynamic model. The analysis results show that consistent predictive traffic information can help to stabilize day-to-day network flow fluctuations and thus can enhance fixed-point stability of the dynamic model. In addition, this paper investigates the effects of travelers' attitudes to risk on the network flow evolution process. An illustrative example is presented to demonstrate the characteristics of the dynamic model as well as to verify the theoretical analysis results.

Language: en