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Statistical methods for comparison of day-to-day traffic models

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  • Hazelton, Martin L.
  • Parry, Katharina

Abstract

Day-to-day dynamic traffic models have considerable potential as tools for transport network management and planning, and also for the study of traveller behaviour. However, their efficacy for these purposes is dependent on appropriate model selection. In particular, while it can be tempting to incorporate sophisticated and intricate representations of traveller learning in day-to-day models, it is important to ask whether the available data are able to support such a level of model complexity. To this end, our overall aim is to investigate the extent to which it is possible to learn about day-to-day traveller behaviour from observations on traffic counts collected over a sequence of days. The paper makes two specific contributions. The first is the development of a principled Bayesian methodology for comparing day-to-day models using link count data, and a description of how it may be implemented in practice using Markov chain Monte Carlo methods. The second contribution is a suite of simulation studies that examine whether these techniques can select the correct model within a set of alternatives with a variety of complexities of behavioural representation. We find that successful model choice based on link count data is often possible when travellers are relatively sensitive to differences in route utilities.

Suggested Citation

  • Hazelton, Martin L. & Parry, Katharina, 2016. "Statistical methods for comparison of day-to-day traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 22-34.
  • Handle: RePEc:eee:transb:v:92:y:2016:i:pa:p:22-34
    DOI: 10.1016/j.trb.2015.08.005
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    References listed on IDEAS

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    Cited by:

    1. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    2. Crawford, F. & Watling, D.P. & Connors, R.D., 2017. "A statistical method for estimating predictable differences between daily traffic flow profiles," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 196-213.
    3. Xiaomei Zhao & Chunhua Wan & Jun Bi, 2019. "Day-to-Day Assignment Models and Traffic Dynamics Under Information Provision," Networks and Spatial Economics, Springer, vol. 19(2), pages 473-502, June.
    4. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    5. Dong-Fan Xie & Xiao-Mei Zhao, 2020. "Traffic Dynamics and Mode Choice’s Delay Effect Under Traffic Restriction in Two-Mode Networks," Networks and Spatial Economics, Springer, vol. 20(3), pages 873-913, September.
    6. Guo, Ren-Yong & Szeto, W.Y., 2018. "Day-to-day modal choice with a Pareto improvement or zero-sum revenue scheme," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 1-25.
    7. Barroso, Joana Maia Fernandes & Albuquerque-Oliveira, João Lucas & Oliveira-Neto, Francisco Moraes, 2020. "Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks," Journal of Transport Geography, Elsevier, vol. 89(C).

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    Keywords

    Bayesian; DIC; MCMC; Network; Route choice;
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