Demand-Driven Train Schedule Synchronization for High-Speed Rail Lines

Huimin Niu, Xiaopeng Tian, Xuesong Zhou

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

This paper addresses a new class of train scheduling problems with two interconnected high-speed rail lines under given and precise time-dependent origin-destination demand input. The proposed systematic schedule synchronization approach focuses on satisfying the requirements of transfer passengers from one rail line to another rail line. Aiming to minimize passenger waiting times at stations and crowding disutility in trains, a nonlinear optimization model for a single-line case is formulated to demonstrate the modeling framework of train scheduling problems. The model is then extended to a two-line case by explicitly taking into account the number of boarding and alighting passengers at the connection station. Using a state-space representation, a novel dynamic programming algorithm is designed to solve the single-line problem as a deterministic finite-state problem with sequential decisions. An integer coding-based genetic algorithm procedure is developed to solve the proposed model for general cases with two lines. A simplified real-world example illustrates that the designed schedule is beneficial to the through passengers and transfer passengers simultaneously.

Original languageEnglish (US)
Article number7091013
Pages (from-to)2642-2652
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume16
Issue number5
DOIs
StatePublished - Oct 1 2015

Keywords

  • demand driven
  • dynamic programming
  • genetic algorithm
  • High-speed rail
  • train schedule synchronization

ASJC Scopus subject areas

  • Automotive Engineering
  • Computer Science Applications
  • Mechanical Engineering

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