Modeling Route Choice Behavior From Smartphone GPS data

Smartphones have the capability of recording various kinds of data from built-in sensors such as GPS in a non-intrusive, systematic way. In order to be used as observations for route choice models, the discrete sequences of GPS data need to be associated with the transportation network to generate meaningful paths. In this paper, a probabilistic path generation algorithm is proposed to replace conventional map matching (MM) algorithms. Instead of giving a unique matching result, the proposed algorithm generates a set of potential true paths. Temporal information (speed and time) is used to calculate the likelihood of the data while traveling on a given path. Comparisons against a state of the art deterministic MM algorithm using real trips recorded from a single user's smartphone are performed so as to illustrate the robustness and effectiveness of the proposed algorithm. Also, a Path-Size Logit (PSL) model is estimated based on a sample of real observations. The estimation results show the viability of applying the proposed method in a real context.