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Identification of an appropriate route choice model to understand travel behavior remains challenging. To this end, Fosgerau et al. (2013) have recently developed a link-based route choice model termed the “recursive logit” (RL) model. A decision-maker is assumed to choose the next link recursively that maximizes the sum of instantaneous utility and expected downstream utility at each node. However, in practical application, some computational issues remain, including large (and often ill-defined) matrix inversions. Here, we develop an alternative RL model that considers the probability of awareness of the next link that improves the stability of model estimations. The model was estimated using vehicle trajectory data from the ETC (Electronic Toll Collection) 2.0 dataset of the Tokyo Metropolitan area, and the results were compared to those of a conventional RL model in terms of predictive accuracy and computational efficiency.

Route Choice Analysis in the Tokyo Metropolitan Area Using a Link-based Recursive Logit Model Featuring Link Awareness