Passenger Route and Departure Time Guidance under Disruptions in Oversaturated Urban Rail Transit Networks

作     者：Zhuo, Siyu Zhu, Xiaoning Shang, Pan Liu, Zhengke 

作者机构：School of Traffic and Transportation Key Laboratory of Transport Industry of Comprehensive Transportation Theory Ministry of Transport Beijing Jiaotong University Beijing100044 China School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control Beihang University Beijing100191 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Travel time 

摘      要：The urban rail transit (URT) system attracts many commuters with its punctuality and convenience. However, it is vulnerable to disruptions caused by factors like extreme weather and temporary equipment failures, which greatly impact passengers journeys and diminish the system s service quality. In this study, we propose targeted travel guidance for passengers at different space-time locations by devising passenger rescheduling strategies during disruptions. This guidance not only offers insights into route changes but also provides practical recommendations for delaying departure times when required. We present a novel three-feature four-group passenger classification principle, integrating temporal, spatial, and spatiotemporal features to classify passengers in disrupted URT networks. This approach results in the creation of four distinct solution spaces based on passenger groups. A mixed integer programming model is built based on individual level considering the First-in-First-out (FIFO) rule in oversaturated networks. Additionally, we present a two-stage solution approach for handling the complex issues in large-scale networks. Experimental results from both small-scale artificial networks and the real-world Beijing URT network validate the efficacy of our proposed passenger rescheduling strategies in mitigating disruptions. Specifically, when compared to scenarios with no travel guidance during disruptions, our strategies achieve a substantial reduction in total passenger travel time by 29.7% and 50.9% respectively, underscoring the effectiveness in managing unexpected disruptions. Copyright © 2024, The Authors. All rights reserved.