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Optimisation of timetable-based, stochastic transit assignment models based on MSA

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  • Otto Nielsen
  • Rasmus Frederiksen

Abstract

Public transport assignment models have increased in complexity in order to describe passengers' route choices as detailed and correctly as possible. Important trends in the development are (1) timetable-based assignment, (2) inclusion of feeder modes, (3) use of stochastic components to describe differences in passengers' preferences within and between purposes and classes (random coefficients), as well as to describe non-explained variation within a utility theory framework, and (4) consideration of capacity problems at coach level, system level and terminal level. In the Copenhagen-Ringsted Model (CRM), such a large-scale transit assignment model was developed and estimated. The Stochastic User Equilibrium problem was solved by the Method of Successive Averages (MSA). However, the model suffered from very large calculation times. The paper focuses on how to optimise transit assignment models based on MSA combined with a generalised utility function. Comparable tests are carried out on a large-scale network. The conclusion is that there is potential of optimising MSA-based methods. Examples of different approaches for this is presented, tested and discussed in the paper. Copyright Springer Science+Business Media, LLC 2006

Suggested Citation

  • Otto Nielsen & Rasmus Frederiksen, 2006. "Optimisation of timetable-based, stochastic transit assignment models based on MSA," Annals of Operations Research, Springer, vol. 144(1), pages 263-285, April.
    • Handle: RePEc:spr:annopr:v:144:y:2006:i:1:p:263-285:10.1007/s10479-006-0012-0
    DOI: 10.1007/s10479-006-0012-0
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    Cited by:

    1. Yao, Rui & Bekhor, Shlomo, 2022. "A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 273-294.
    2. Morten Eltved & Otto Anker Nielsen & Thomas Kjær Rasmussen, 2019. "An assignment model for public transport networks with both schedule- and frequency-based services," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 769-793, December.
    3. Christensen, Linda & Nielsen, Otto Anker & Rich, Jeppe & Knudsen, Mette, 2020. "Optimizing airport infrastructure for a country: The case of Greenland," Research in Transportation Economics, Elsevier, vol. 79(C).
    4. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    5. Jesper Bláfoss Ingvardson & Jonas Kornerup Jensen & Otto Anker Nielsen, 2017. "Analysing improvements to on-street public transport systems: a mesoscopic model approach," Public Transport, Springer, vol. 9(1), pages 385-409, July.
    6. Rich, Jeppe & Nielsen, Otto Anker, 2007. "A socio-economic assessment of proposed road user charging schemes in Copenhagen," Transport Policy, Elsevier, vol. 14(4), pages 330-345, July.
    7. Parbo, Jens & Nielsen, Otto A. & Prato, Carlo G., 2018. "Reducing passengers’ travel time by optimising stopping patterns in a large-scale network: A case-study in the Copenhagen Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 197-212.
    8. 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.
    9. Marie Karen Anderson & Otto Anker Nielsen & Carlo Giacomo Prato, 2017. "Multimodal route choice models of public transport passengers in the Greater Copenhagen Area," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 221-245, September.
    10. Sun, S. & Szeto, W.Y., 2018. "Logit-based transit assignment: Approach-based formulation and paradox revisit," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 191-215.
    11. Oyama, Yuki & Hara, Yusuke & Akamatsu, Takashi, 2022. "Markovian traffic equilibrium assignment based on network generalized extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 135-159.
    12. Jiang, Y. & Szeto, W.Y., 2016. "Reliability-based stochastic transit assignment: Formulations and capacity paradox," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 181-206.
    13. Hamdouch, Younes & Szeto, W.Y. & Jiang, Y., 2014. "A new schedule-based transit assignment model with travel strategies and supply uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 35-67.
    14. 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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