Optimal reassignment of passengers to trains following a broken train

Weining Hao, Lingyun Meng, Lucas Veelenturf, Sihui Long, Francesco Corman, Xiaoyong Niu

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Train rescheduling is an important task in railway operations to ensure a high punctuality and reliability and thereby also high service level for passengers. In a railway system with ticket booking mechanism, the train dispatching task is quite different and more challenging compared with a system without ticket booking mechanism. In particular when there are severe disruptions where multiple trains are cancelled. This paper focuses on the problem of train rescheduling under disruptions, while simultaneously considering passenger reassignments. A mixed-integer linear programming model is developed to maximize the number of transported passengers who are influenced by the impacted train and to minimize total delays at the same time. A'big-M' method is also used to represent complex 'if-then' mapping constraints between passenger reassignment and train stopping constraints. A set of numerical experiments is presented based on a railway network to demonstrate the validity and efficiency of the model.

LanguageEnglish
Title of host publication2018 International Conference on Intelligent Rail Transportation, ICIRT 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-5386-7528-1
ISBN (Print)978-1-5386-7529-8
DOIs
StatePublished - 13 Feb 2019
Event2018 International Conference on Intelligent Rail Transportation, ICIRT 2018 - Singapore, Singapore
Duration: 12 Dec 201814 Dec 2018

Conference

Conference2018 International Conference on Intelligent Rail Transportation, ICIRT 2018
CountrySingapore
CitySingapore
Period12/12/1814/12/18

Fingerprint

Railway
Rescheduling
German Federal Railways
Linear programming
Dispatching
Mixed Integer Linear Programming
Service Levels
Programming Model
Linear Model
programming
Maximise
Numerical Experiment
Minimise
efficiency
Experiments
experiment
Demonstrate
Train
Model

Keywords

  • mixed integer programming
  • passenger reassignment
  • train rescheduling

Cite this

Hao, W., Meng, L., Veelenturf, L., Long, S., Corman, F., & Niu, X. (2019). Optimal reassignment of passengers to trains following a broken train. In 2018 International Conference on Intelligent Rail Transportation, ICIRT 2018 [8641524] Piscataway: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/ICIRT.2018.8641524
Hao, Weining ; Meng, Lingyun ; Veelenturf, Lucas ; Long, Sihui ; Corman, Francesco ; Niu, Xiaoyong. / Optimal reassignment of passengers to trains following a broken train. 2018 International Conference on Intelligent Rail Transportation, ICIRT 2018. Piscataway : Institute of Electrical and Electronics Engineers (IEEE), 2019.
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Hao, W, Meng, L, Veelenturf, L, Long, S, Corman, F & Niu, X 2019, Optimal reassignment of passengers to trains following a broken train. in 2018 International Conference on Intelligent Rail Transportation, ICIRT 2018., 8641524, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, 2018 International Conference on Intelligent Rail Transportation, ICIRT 2018, Singapore, Singapore, 12/12/18. DOI: 10.1109/ICIRT.2018.8641524

Optimal reassignment of passengers to trains following a broken train. / Hao, Weining; Meng, Lingyun; Veelenturf, Lucas; Long, Sihui; Corman, Francesco; Niu, Xiaoyong.

2018 International Conference on Intelligent Rail Transportation, ICIRT 2018. Piscataway : Institute of Electrical and Electronics Engineers (IEEE), 2019. 8641524.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Hao W, Meng L, Veelenturf L, Long S, Corman F, Niu X. Optimal reassignment of passengers to trains following a broken train. In 2018 International Conference on Intelligent Rail Transportation, ICIRT 2018. Piscataway: Institute of Electrical and Electronics Engineers (IEEE). 2019. 8641524. Available from, DOI: 10.1109/ICIRT.2018.8641524