Dynamic passenger demand-oriented train scheduling optimization considering flexible short-turning strategy

Liya Yang, Yu Yao, Hua Shi and Pan Shang

Journal of the Operational Research Society, 2021, vol. 72, issue 8, 1707-1725

Abstract: In this study, we focus on improving the efficiency of an urban rail transit line under the circumstance of spatially unbalanced passenger demand. A flexible short-turning strategy is integrated into the train scheduling problem, aiming to obtain a train timetable and the corresponding circulation plan adapted to a time-dependent passenger demand. First, we formulate the dynamic passenger demand-oriented train scheduling problem as a multi-commodity network flow optimization model in a two-layer space-time network. The proposed model is then decomposed into train scheduling and passenger assignment sub-problems by relaxing the coupling constraint. Therefore, an optimal solution of the original model can be obtained by iteratively solving two easy-to-solve sub-problems in a Lagrangian relaxation solution framework. The effectiveness of the model is evaluated using a series of simple experiments and a real-world case study based on the Beijing Yizhuang Line.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2020.1806745 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:72:y:2021:i:8:p:1707-1725

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2020.1806745

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().