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
Number of pages13
JournalTransportation research record
DOIs
Publication statusE-pub ahead of print/First online - 10 Mar 2019

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Travel time
Planning
Genetic algorithms

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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.",
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Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization. / Gkiotsalitis, Konstantinos.

In: Transportation research record, 10.03.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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