A decomposition method for estimating recursive logit based route choice models
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Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We design a decomposition (DeC) method in order to reduce the number of linear systems to be solved, opening the possibility to estimate more complex RL based models, for instance mixed RL models. We test the performance of the DeC method by estimating the RL model on two networks of more than 7000 and 40,000 links, and we show that the DeC method significantly reduces the estimation time. We also use the DeC method to estimate two mixed RL specifications, one using random coefficients and one incorporating error components associated with subnetworks (Frejinger and Bierlaire 2007). The models are estimated on a real network and a cross-validation study is performed. The results suggest that the mixed RL models can be estimated in a reasonable time with the DeC method. These models yield sensible parameter estimates and the in-sample and out-of sample fits are significantly better than the RL model.
KeywordsDecomposition method Route choice Mixed recursive logit models Subnetworks Cross-validation
This research was funded by Natural Sciences and Engineering Research Council of Canada (NSERC), discovery grant 435678-2013. We have benefited from valuable discussions with Mogens Fosgerau. The paper has also been improved thanks to the comments of the three anonymous reviewers.
- Bekhor S, Ben-Akiva M, Ramming M (1805) Adaptation of logit kernel to route choice situation. Transp Res Record 78–85:2002Google Scholar
- Ben-Akiva M, Bierlaire M (1999) Discrete choice methods and their applications to short-term travel decisions. In: Hall R (ed.) Handbook of Transportation Science, pp 5–34. KluwerGoogle Scholar
- Ben-Akiva M (1973) The structure of travel demand models. PhD thesis, MITGoogle Scholar
- Bolduc D, Ben-Akiva M (1991) A multinomial probit formulation for large choice sets. In: Proceedings of the 6th International Conference on Travel Behaviour, vol 2, pp 243–258Google Scholar
- McFadden D (1978) Modelling the choice of residential location. In: Karlqvist A, Lundqvist L, Snickars F, Weibull J (eds) Spatial Interaction Theory and Residential Location. North-Holland, Amsterdam, pp 75–96Google Scholar
- Nocedal J, Wright SJ (2006) Numer Optim, 2nd edn. Springer, New York, NY, USAGoogle Scholar
- Train K (2003) Discrete choice methods with simulation. Cambridge University PressGoogle Scholar
- Zimmermann M, Mai T, Frejinger E (2016) Bike route choice modeling using GPS data without choice sets of paths. CIRRELT-2016-49, Interuniversity Research Centre on Enterprise Networks, Logistics and TransportationGoogle Scholar