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Day-to-Day Assignment Models and Traffic Dynamics Under Information Provision

Xiaomei Zhao (), Chunhua Wan () and Jun Bi ()
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Xiaomei Zhao: Beijing Jiaotong University
Chunhua Wan: Hisense TransTech Co., Ltd.
Jun Bi: Beijing Jiaotong University

Networks and Spatial Economics, 2019, vol. 19, issue 2, 473-502

Abstract: Abstract Traffic networks are often perturbed by temporal events, which generally cause capacity on the links to drop. A capacity drop can lead to fluctuations in flows and travel costs and can further cause congestion. Most day-to-day models have focused on how to describe traveler choice dynamics based on general information, but few studies have considered how to design reasonable information to guide traveler choice behaviors under day-to-day dynamics. In this paper, a day-to-day traffic assignment model is proposed that considers the cost difference between the estimated travel cost on the current day and the actual cost on the previous day to capture traveler path-switching behaviors under advanced information. Our aim is to better understand the dynamic evolution of network flow under information provision. In the model, both the travel cost and cost difference have influences on traveler path-switching behaviors. The linear stability of the equilibrium is analyzed. Compared with the system without the cost difference, the introduction of the cost difference appears to enlarge the stable equilibrium region and reduce the fluctuations in cost and flow under reasonable parameter settings. Additionally, three scenarios are used to examine the influence of the cost difference under a capacity drop. The results show that the cost difference can avoid rapid propagation of congestion induced by a temporal capacity drop. This study explains the influence of the cost difference on the evolutionary pattern of the transportation networks and offers guidance for network design and management.

Keywords: Traffic dynamics; Route choice; Traffic assignment; Day-to-day dynamic system; Cost difference; Stability analysis (search for similar items in EconPapers)
Date: 2019
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