Citation

Escribano-Macias JJ, Angeloudis P, Han K. Transportmetrica A: Transp. Sci. 2020; 16(3): 1079-1110.

Copyright

(Copyright © 2020, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2020.1725179

PMID

unavailable

Abstract

Large-scale evacuations constitute common life-saving exercises that are activated in many disaster response campaigns. Their effectiveness is often inhibited by traffic congestion, disrupted and imperfect coordination mechanisms, and the poor state of the underlying transportation networks. To address this problem, this paper presents a hybrid simulation-optimisation methodology to optimise evacuation response strategies through demand staging and signal phasing. We introduce a pre-planning model that evaluates evacuation policies, using a low-level dynamic traffic assignment model that captures the effects of congestion, queuing and vehicle spillback. Optimal strategies are determined using derivative-free optimisation algorithms, applied to an evacuation problem based on a benchmark dataset. The effects of varying the number of activated paths and the frequency of departure under different network conditions are observed. Our analysis indicates that combined departure time scheduling and signal phasing is a promising method to improve evacuation efficiency when compared to a worst-case benchmark scenario.

Language: en

Keywords

disaster evacuation; dynamic traffic assignment; genetic algorithms; Simulation-optimisation