Artificial Intelligence and Robotics Commons^™

A Decomposition Method For Estimating Recursive Logit Based Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger

Research Collection School Of Computing and Information Systems

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 …