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Path Size Logit route choice models: Issues with current models, a new internally consistent approach, and parameter estimation on a large-scale network with GPS data

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  • Duncan, Lawrence Christopher
  • Watling, David Paul
  • Connors, Richard Dominic
  • Rasmussen, Thomas Kjær
  • Nielsen, Otto Anker

Abstract

Path Size Logit route choice models attempt to capture the correlation between routes by including correction terms within the route utility functions. This provides a convenient closed-form solution for implementation in traffic network models. The path size terms measure distinctiveness of routes; a route is penalised based on the number of other routes sharing its links, and the costs of those shared links. Typically, real road networks have many very long routes that should be considered unrealistic. Such unrealistic routes are problematic for the Path Size Logit (PSL) model because they negatively impact the choice probabilities of realistic routes when links are shared. The Generalised Path Size Logit (GPSL) model attempts to address this problem by weighting the contributions of routes to path size terms according to the ratio of route travel costs. However, the GPSL model is not internally consistent in how it defines routes as being unrealistic: the path size terms consider only travel cost, whereas the route choice probability relation considers disutility including the correction term.

Suggested Citation

  • Duncan, Lawrence Christopher & Watling, David Paul & Connors, Richard Dominic & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2020. "Path Size Logit route choice models: Issues with current models, a new internally consistent approach, and parameter estimation on a large-scale network with GPS data," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 1-40.
  • Handle: RePEc:eee:transb:v:135:y:2020:i:c:p:1-40
    DOI: 10.1016/j.trb.2020.02.006
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    4. Yuki Oyama, 2023. "Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes," Papers 2307.08646, arXiv.org.
    5. Li, Dawei & Feng, Siqi & Song, Yuchen & Lai, Xinjun & Bekhor, Shlomo, 2023. "Asymmetric closed-form route choice models: Formulations and comparative applications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    6. Marra, Alessio D. & Sun, Linghang & Corman, Francesco, 2022. "The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area," Transport Policy, Elsevier, vol. 116(C), pages 258-268.
    7. Knies, Austin & Lorca, Jorge & Melo, Emerson, 2022. "A recursive logit model with choice aversion and its application to transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 47-71.
    8. Nielsen, Otto Anker & Eltved, Morten & Anderson, Marie Karen & Prato, Carlo Giacomo, 2021. "Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 76-92.

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