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

TL;DR

This paper develops a novel passenger guidance system for oversaturated urban rail networks during disruptions, using classification and optimization to reduce travel times significantly.

Contribution

It introduces a new passenger classification principle and a mixed integer programming model for effective rescheduling during disruptions in oversaturated URT networks.

Findings

Travel time reduced by up to 50.9% with guidance strategies

Effective classification improves passenger rescheduling accuracy

Validated on real-world Beijing URT network

Abstract

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 spatio-temporal 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.