Research Area:  Wireless Sensor Networks
Advanced information is increasingly being used as an external intervention tool to positively influence system performance. In many traffic assignment problems, the proportion of travellers that reroute is assumed to be constant (static rerouting behavior), whereas the number of travellers that modify their routes will change dynamically with the cost difference (dynamic rerouting behavior). In this paper, dynamic rerouting behavior is considered in day-to-day traffic assignment models to capture travellers reactions to advanced information. The properties of a dynamic rerouting weight function are studied using survey data. Our goal is to better understand the dynamic evolution of network flow. In the model, the rerouting weight varies dynamically with the cost difference between travellers estimated and expected costs. The linear stability of the equilibrium is analyzed. Both theoretical analyses and numerical simulations indicated that dynamic rerouting behavior increases the stability domain and decreases the parameter sensitivity. Additionally, the dynamic evolution of the cost and flow near the stability boundary is studied. The results show that the dynamic rerouting behavior helps to improve the convergence speed and dampen the oscillations in the evolution process. This paper explains the influence of dynamic rerouting choice behavior on the evolution patterns of transportation networks and provides guidance for network design and management.
Author(s) Name:  Xiaomei Zhao; Chunhua Wan; Huijun Sun; Dongfan Xie and Ziyou Gao
Journal name:   IEEE Transactions on Intelligent Transportation Systems
Publisher name:  IEEE
Volume Information:  Volume: 18, Issue: 10, Oct. 2017,Page(s): 2763 - 2779
Paper Link:   https://ieeexplore.ieee.org/abstract/document/7896610