Fast train: A computationally efficient train routing and scheduling engine for general rail networks

Lingyun Meng, Xuesong Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A number of tactical and operational applications, such as train timetabling and dispatching, require sophisticated and computationally efficient software to optimize train schedules. An open-source train scheduling package, namely FastTrain has been designed and implemented to generate feasible schedules and optimality gap estimates for trains on a generic rail network with both single and double tracks. This paper describes its two major modelling components: (1) a network cumulative flow model to capture the complex safety rules of infrastructure resources, and (2) a Lagrangian relaxation solution framework which efficiently decomposes the original multi-trains complex model to a set of single train oriented subproblems. Two experimental cases based on the adapted datasets released by INFORMS RAS are conducted to demonstrate the effectiveness and efficiency of the developed algorithm under different network and data availability conditions.

Original languageEnglish (US)
Title of host publication2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2416-2421
Number of pages6
ISBN (Electronic)9781479960781
DOIs
StatePublished - Nov 14 2014
Event2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 - Qingdao, China
Duration: Oct 8 2014Oct 11 2014

Publication series

Name2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014

Other

Other2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Country/TerritoryChina
CityQingdao
Period10/8/1410/11/14

Keywords

  • Lagrangian Relaxation
  • Resource Constraints
  • Shortest Path Algorithm
  • Train Routing
  • Train Scheduling

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Mechanical Engineering

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