Reinforcement Learning-Based Bus Holding for High-Frequency Services

Francesco Alesiani, Konstantinos Gkiotsalitis

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

    2 Citations (Scopus)

    Abstract

    Since the bus holding problem is an operational control problem, bus holding decisions should be made in realtime. For this reason, common bus holding approaches, such as the one-headway-based holding, focus on computationally inexpensive, rule-based techniques that try to minimize the deviation of the actual headways from the planned ones. Nevertheless, rule-based methods optimize the system locally without considering the full effect of the bus holding decisions to future trips or other performance indicators. For this reason, this work introduces a Reinforcement Learning approach which is capable of making holistic bus holding decisions in realtime after the completion of a training period. The proposed approach is trained in a circular bus line in Singapore using 400 episodes (where an episode is one day of operations) and evaluated using 200 episodes demonstrating a significant improvement in scenarios with strong travel time disturbances and a slight improvement in scenarios with low travel time variations.
    Original languageEnglish
    Title of host publication2018 21st International Conference on Intelligent Transportation Systems (ITSC)
    PublisherIEEE
    ISBN (Electronic)978-1-7281-0323-5
    ISBN (Print)978-1-7281-0321-1
    DOIs
    Publication statusPublished - 2018
    Event21st IEEE International Conference on Intelligent Transportation Systems 2018 - Maui, United States
    Duration: 4 Nov 20187 Nov 2018
    Conference number: 21
    https://www.ieee-itsc2018.org/

    Conference

    Conference21st IEEE International Conference on Intelligent Transportation Systems 2018
    Abbreviated titleITSC 2018
    CountryUnited States
    CityMaui
    Period4/11/187/11/18
    Internet address

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