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A spatially disaggregated model for the technology selection and design of a transit line

Author

Listed:
  • Luigi Moccia

    (Istituto di Calcolo e Reti ad Alte Prestazioni
    Logistics and Transportation (CIRRELT))

  • Duncan W. Allen

    (IBI Group)

  • Gilbert Laporte

    (Logistics and Transportation (CIRRELT)
    3000 chemin de la Côte-Sainte-Catherine
    University of Bath)

Abstract

Our research question is the usefulness of a high level of spatial granularity for the travel demand when planning a transit line. We formulate a new optimization model for the technology selection and design of a transit line where the spatial attributes of the travel demand can be finely set. The solution method relies on approximated formulae, and we establish relationships with a classic result for the optimal stop spacing. We also present a refinement of the in-vehicle passenger crowding for an existing transit design model where demand spatial attributes are set synthetically. We call “spatially disaggregate” and “spatially aggregate” the former and the latter model, respectively. These two models are compared by numerical experiments on a scenario for three semi-rapid transit technologies where two variants consider opposite demand profiles in terms of spatial distribution. We conclude that the spatially aggregated model is sufficient when the main goal is technology selection, whereas the spatially disaggregate model is better for design and benchmarking purposes.

Suggested Citation

  • Luigi Moccia & Duncan W. Allen & Gilbert Laporte, 2020. "A spatially disaggregated model for the technology selection and design of a transit line," Public Transport, Springer, vol. 12(3), pages 647-691, October.
    • Handle: RePEc:spr:pubtra:v:12:y:2020:i:3:d:10.1007_s12469-020-00250-0
    DOI: 10.1007/s12469-020-00250-0
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    References listed on IDEAS

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    1. Tirachini, Alejandro & Hensher, David A. & Jara-Díaz, Sergio R., 2010. "Restating modal investment priority with an improved model for public transport analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 1148-1168, November.
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    8. Moccia, Luigi & Giallombardo, Giovanni & Laporte, Gilbert, 2017. "Models for technology choice in a transit corridor with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 733-756.
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    10. Luigi Moccia & Duncan W. Allen & Eric C. Bruun, 2018. "A technology selection and design model of a semi-rapid transit line," Public Transport, Springer, vol. 10(3), pages 455-497, December.
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    Cited by:

    1. Gülçin Canbulut & Erkan Köse & Oğuzhan Ahmet Arik, 2022. "Public transportation vehicle selection by the grey relational analysis method," Public Transport, Springer, vol. 14(2), pages 367-384, June.
    2. Basnak, Paul & Giesen, Ricardo & Muñoz, Juan Carlos, 2022. "Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 140-156.
    3. Luigi Moccia & Duncan W. Allen & Gilbert Laporte & Andrea Spinosa, 2022. "Mode boundaries of automated metro and semi-rapid rail in urban transit," Public Transport, Springer, vol. 14(3), pages 739-802, October.

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