Estimating route choice models using low frequency GPS data.

Abstract: GPS data are increasingly available to be used in transportation planning. Route choice models are estimated to address the behavior of individuals choosing a route in a given network. When data is collected with low frequency, it is unknown which path was traversed between the GPS data points. Furthermore, GPS data has measurements error. In this thesis we design an algorithm to consistently estimate a given route choice model in the presence of sparse GPS data and measurement errors. We present an extension on a new method presented by Kalström et al. (2011) to estimate a route choice model. This method focuses on a given simple way to estimate the true parameter of a model. For this purpose the indirect inference method is employed as a structured procedure. In our context, a simple multinomial logit model is used as the auxiliary model with the simulated data sets and in a structured way returns the estimated parameter. This version of discrete choice model is simple and fast which qualifies it as an appropriate auxiliary model. We estimate a model with random link costs which allows for a natural correlation structure across paths and is also useful for simulating paths in order to make choice sets. In this study Monte Carlo evidence is provided to show the feasibility and accuracy of the proposed algorithm using a real world network from Borlänge, Sweden. The main conclusion is that indirect inference is an exciting option in the tool box for route choice estimation which can be used for estimating route choice models using low frequency GPS sampling data.