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Capturing correlation with subnetworks in route choice models

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  • Frejinger, E.
  • Bierlaire, M.
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    Abstract

    When using random utility models for a route choice problem, a critical issue is the significant correlation among alternatives. There are basically two types of models proposed in the literature to address it: (i) a deterministic correction of the path utilities in a Multinomial Logit model (such as the Path Size Logit or the C-Logit models) and (ii) an explicit modeling of the correlation through assumptions about the error terms, and the use of advanced discrete choice models such as the Cross-Nested Logit or the Error Component models. The first is simple, easy to handle and often used in practice. Unfortunately, it does not correctly capture the correlation structure, as we discuss in details in the paper. The second is more consistent with the modeling objectives, but very complicated to specify and estimate. The modeling framework proposed in this paper allows the analyst to control the trade-off between the simplicity of the model and the level of realism. Within this framework, the key concept capturing the correlation structure is called a subnetwork. A subnetwork is a simplification of the road network only containing easy identifiable and behaviorally relevant roads. In practice, the subnetwork can easily be defined based on the route network hierarchy. The importance and the originality of our approach lie in the possibility to capture the most important correlation without considerably increasing the model complexity. This makes it suitable for a wide spectrum of applications, namely involving realistic large-scale networks. As an illustration, we present estimation results of a factor analytic specification of a mixture of Multinomial Logit model, where the correlation among paths is captured by error components. The estimation is based on a GPS dataset collected in the Swedish city of Borlänge. The results show a significant increase in model fit and forecasting performance for the Error Component model compared to a Path Size Logit model. Moreover, the correlation parameters are significant.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 41 (2007)
    Issue (Month): 3 (March)
    Pages: 363-378

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    Handle: RePEc:eee:transb:v:41:y:2007:i:3:p:363-378

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    References

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    1. Azevedo, JoseAugusto & Santos Costa, Maria Emilia O. & Silvestre Madeira, Joaquim Joao E. R. & Vieira Martins, Ernesto Q., 1993. "An algorithm for the ranking of shortest paths," European Journal of Operational Research, Elsevier, vol. 69(1), pages 97-106, August.
    2. Cascetta, Ennio & Russo, Francesco & Viola, Francesco A. & Vitetta, Antonino, 2002. "A model of route perception in urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 577-592, August.
    3. Yai, Tetsuo & Iwakura, Seiji & Morichi, Shigeru, 1997. "Multinomial probit with structured covariance for route choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 31(3), pages 195-207, June.
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    Cited by:
    1. Chen, Anthony & Pravinvongvuth, Surachet & Xu, Xiangdong & Ryu, Seungkyu & Chootinan, Piya, 2012. "Examining the scaling effect and overlapping problem in logit-based stochastic user equilibrium models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1343-1358.
    2. Fosgerau, Mogens & Frejinger, Emma & Karlstrom, Anders, 2013. "A link based network route choice model with unrestricted choice set," MPRA Paper 48707, University Library of Munich, Germany.
    3. Frejinger, E. & Bierlaire, M. & Ben-Akiva, M., 2009. "Sampling of alternatives for route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 984-994, December.
    4. Fadaei Oshyani, Masoud & Sundberg, Marcus & Karlström, Anders, 2013. "Consistently estimating link speed using sparse GPS data with measured errors," Working papers in Transport Economics 2013:12, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    5. Bekhor, Shlomo & Prato, Carlo Giacomo, 2009. "Methodological transferability in route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 422-437, May.
    6. Habib, Khandker Nurul & Morency, Catherine & Trépanier, Martin & Salem, Sarah, 2013. "Application of an independent availability logit model (IAL) for route choice modelling: Considering bridge choice as a key determinant of selected routes for commuting in Montreal," Journal of choice modelling, Elsevier, vol. 9(C), pages 14-26.
    7. Shanjiang Zhu & David Levinson, 2011. "A Portfolio Theory of Route Choice," Working Papers 000096, University of Minnesota: Nexus Research Group.
    8. Broach, Joseph & Dill, Jennifer & Gliebe, John, 2012. "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1730-1740.
    9. Carlo Prato & Shlomo Bekhor & Cristina Pronello, 2012. "Latent variables and route choice behavior," Transportation, Springer, vol. 39(2), pages 299-319, March.
    10. Kitthamkesorn, Songyot & Chen, Anthony, 2013. "A path-size weibit stochastic user equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 378-397.
    11. Gao, Song & Frejinger, Emma & Ben-Akiva, Moshe, 2011. "Cognitive cost in route choice with real-time information: An exploratory analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 916-926, November.
    12. He, Xiaozheng & Guo, Xiaolei & Liu, Henry X., 2010. "A link-based day-to-day traffic assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 597-608, May.
    13. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    14. Fadaei Oshyani, Masoud & Sundberg, Marcus & Karlström, Anders, 2013. "Estimating flexible route choice models using sparse data," Working papers in Transport Economics 2013:11, CTS - Centre for Transport Studies Stockholm (KTH and VTI).

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