Probabilistic multimodal map-matching with rich smartphone data

This article proposes a probabilistic method that infers the transport modes and the physical paths of trips from smartphone data that were recorded during travels. This method synthesizes multiple kinds of data from smartphone sensors, which provide relevant location or transport mode information: global positioning system (GPS), Bluetooth, and accelerometer. The method is based on a smartphone measurement model that calculates the likelihood of observing the smartphone data in the multimodal transport network. The output of this probabilistic method is a set of candidate true paths and the probability of each path being the true one. The transport mode used on each arc is also inferred. Numerical experiments include map visualizations of some example trips and an analysis on the performance of the transport mode inference.