Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization

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    Abstract

    The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from those expected. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategies
    which are robust to travel-time variations. In more detail, we show how historical travel-time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of five months of data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop-skipping strategy.
    Original languageEnglish
    Pages (from-to)611-623
    Number of pages13
    JournalTransportation research record
    Volume2673
    Issue number3
    Early online date10 Mar 2019
    DOIs
    Publication statusPublished - Mar 2019

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    Travel time
    Planning
    Genetic algorithms

    Cite this

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    title = "Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization",
    abstract = "The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from those expected. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategieswhich are robust to travel-time variations. In more detail, we show how historical travel-time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of five months of data from a circular bus line in Singapore demonstrating an improved performance of more than 10{\%} in worst-case scenarios which encourages further investigation of the robust stop-skipping strategy.",
    author = "Konstantinos Gkiotsalitis",
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    Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization. / Gkiotsalitis, Konstantinos.

    In: Transportation research record, Vol. 2673, No. 3, 03.2019, p. 611-623.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization

    AU - Gkiotsalitis, Konstantinos

    PY - 2019/3

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    AB - The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from those expected. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategieswhich are robust to travel-time variations. In more detail, we show how historical travel-time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of five months of data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop-skipping strategy.

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