An integrated model for evacuation routing and traffic signal optimization with background demand uncertainty. Issue 1 (1st October 2012)

• Record Type: Journal Article
• Authors: Ren, Gang; Huang, Zhengfeng; Cheng, Yang; Zhao, Xing; Zhang, Yong
• Other Names: Lam William H. K. guestEditor.
• Is Part Of: Journal of advanced transportation. Volume 47:Issue 1(2013:Jan.)
• Page Start: 4
• Page End: 27
• Publication Date: 2012-10-01
• Subjects: evacuation route -- traffic signal -- uncertain background travel demand -- bi‐objective bi‐level programming model -- robust optimization -- NSGA‐II; Transportation -- Periodicals; 388.05
• DOI: 10.1002/atr.1211
• Languages: English
• ISSNs: 0197-6729

Abstract

SUMMARY: Emergency evacuation in congested urban networks can be influenced by uncertain background travel demands, but this issue has not been fully investigated. In this study, evacuation links are prepared for exclusive use by evacuees. Under this assumption, an integrated model is proposed for determining flows on emergency evacuation routes and traffic signals at intersections in the presence of uncertain background travel demands. The problem is formulated as a bi‐objective bi‐level programming model based on the concept of robust optimization. It is assumed that background travel demands belong to an ellipsoidal likelihood region whose parameters are determined by a singly constrained gravity model. With the aim of maximizing the background traffic impact degree in the lower‐level model with a logit‐based stochastic assignment constraint and background demands constraint (the aforementioned ellipsoidal likelihood region), background traffic corresponding to worst‐case demands is determined by the Lagrange multiplier method. In the upper‐level model, two objectives, minimizing both the total travel time of evacuation flows and performance index of the whole network flows, are constructed to determine optimal evacuation flows and traffic signals. The Non‐dominated Sorting Genetic Algorithm II is employed to determine the Pareto solutions of this optimization problem. An example using Sioux Falls networks illustrates the validity of the algorithm. A field case involving the Jianye network around the Nanjing Olympics Sports Center shows the applicability of this algorithm. Copyright © 2012 John Wiley & Sons, Ltd.