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An adaptive scaled network for public transport route optimisation

Author

Listed:
  • Philipp Heyken Soares

    (University of Nottingham)

  • Christine L. Mumford

    (Cardiff University)

  • Kwabena Amponsah

    (University of Nottingham)

  • Yong Mao

    (University of Nottingham)

Abstract

We introduce an adaptive network for public transport route optimisation by scaling down the available street network to a level where optimisation methods such as genetic algorithms can be applied. Our scaling is adapted to preserve the characteristics of the street network. The methodology is applied to the urban area of Nottingham, UK, to generate a new benchmark dataset for bus route optimisation studies. All travel time and demand data as well as information of permitted start and end points of routes, are derived from openly available data. The scaled network is tested with the application of a genetic algorithm adapted for restricted route start and end points. The results are compared with the real-world bus routes.

Suggested Citation

  • Philipp Heyken Soares & Christine L. Mumford & Kwabena Amponsah & Yong Mao, 2019. "An adaptive scaled network for public transport route optimisation," Public Transport, Springer, vol. 11(2), pages 379-412, August.
    • Handle: RePEc:spr:pubtra:v:11:y:2019:i:2:d:10.1007_s12469-019-00208-x
    DOI: 10.1007/s12469-019-00208-x
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    References listed on IDEAS

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    1. Arbex, Renato Oliveira & da Cunha, Claudio Barbieri, 2015. "Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 355-376.
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    Cited by:

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