Vehicle scheduling under stochastic trip times: An approximate dynamic programming approach

Due to unexpected demand surge and supply disruptions, road traffic conditions could exhibit substantial uncertainty, which often makes bus travelers encounter start delays of service trips and substantially degrades the performance of an urban transit system. Meanwhile, rapid advances of information and communication technologies have presented tremendous opportunities for intelligently scheduling a bus fleet. With the full consideration of delay propagation effects, this paper is devoted to formulating the stochastic dynamic vehicle scheduling problem, which dynamically schedules an urban bus fleet to tackle the trip time stochasticity, reduce the delay and minimize the total costs of a transit system. To address the challenge of “curse of dimensionality”, the authors adopt an approximate dynamic programming approach (ADP) where the value function is approximated through a three-layer feed-forward neural network so that the authors are capable of stepping forward to make decisions and solving the Bellman’s equation through sequentially solving multiple mixed integer linear programs. Numerical examples based on the realistic operations dataset of bus lines in Beijing have demonstrated that the proposed neural-network-based ADP approach not only exhibits a good learning behavior but also significantly outperforms both myopic and static polices, especially when trip time stochasticity is high.

• Record URL:
  https://doi.org/10.1016/j.trc.2018.09.010
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X18312786
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • He, Fang
  • Yang, Jie
  • Li, Meng
• Publication Date: 2018-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 144-159
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 96
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Bus transit; Delays; Dynamic programming; Mixed integer programming; Scheduling; Travel time; Urban transit; Vehicle fleets
• Geographic Terms: Beijing (China)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01684384
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 26 2018 5:15PM