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Public transport network optimisation in PTV Visum using selection hyper-heuristics

Author

Listed:
  • Philipp Heyken Soares

    (Nottingham University)

  • Leena Ahmed

    (Cardiff University)

  • Yong Mao

    (Nottingham University)

  • Christine L Mumford

    (Cardiff University)

Abstract

Despite the progress in the field of automatic public transport route optimisation in recent years, there exists a clear gap between the development of optimisation algorithms and their applications in real-world planning processes. In this study, we bridge this gap by developing an interface between the urban transit routing problem (UTRP) and the professional transport modelling software PTV Visum. The interface manages the differences in data requirements between the two worlds of research and allows the optimisation of public transport lines in Visum network models. This is demonstrated with the application of selection hyper-heuristics on two network models representing real-world urban areas. The optimisation objectives include the passengers’ average travel time and operators’ costs. Furthermore, we show how our approach can be combined with a mode choice model to optimise the use of public transport in relation to other modes. This feature is applied in a special optimisation experiment to reduce the number of private vehicles on a selected set of links in the network. The results demonstrate the successful implementation of our interface and the applied optimisation methods for a multi-modal public transport network.

Suggested Citation

  • Philipp Heyken Soares & Leena Ahmed & Yong Mao & Christine L Mumford, 2021. "Public transport network optimisation in PTV Visum using selection hyper-heuristics," Public Transport, Springer, vol. 13(1), pages 163-196, March.
    • Handle: RePEc:spr:pubtra:v:13:y:2021:i:1:d:10.1007_s12469-020-00249-7
    DOI: 10.1007/s12469-020-00249-7
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    References listed on IDEAS

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    1. Ahmed, Leena & Mumford, Christine & Kheiri, Ahmed, 2019. "Solving urban transit route design problem using selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 274(2), pages 545-559.
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    Cited by:

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