Two-stage optimization of urban rail transit formation and real-time station control at comprehensive transportation hub

TL;DR

This paper presents a two-stage optimization model using genetic algorithms to improve train formation and station control, effectively managing passenger demand fluctuations at urban transit hubs.

Contribution

It introduces a novel integrated two-stage model combining train formation and real-time station control for urban rail transit.

Findings

The model improves handling of demand fluctuations.

Genetic algorithm effectively optimizes train schedules and control.

Enhanced coordination at transportation hubs.

Abstract

This paper tries to discuss two strategies of dealing with this complex passenger demand from two aspects: transit train formation and real-time holding control. The genetic algorithm (GA) is designed to solve the integrated two-stage model of optimizing the number, timetable and real-time holding control of the multi-marshalling operated trains. The numerical results show that the combined two-stage model of multi-marshalling operation and holding control at stations can better deal with the demand fluctuation of urban rail transit connecting with the comprehensive transportation hub.