On Extracting Commuter Information from GPS Motion Data

Commuters rely on realistic and real-time information in order to optimize the time spent on commuting between home and work. Delays in (urban) transport and congestion for individual motorized transport are a major issue for unnecessary long travel times. While some of these delays occur randomly, there is also a systematic component. In this paper we describe a data-driven approach to analyze positions of an individual collected using GPS to obtain information on the individual´s typical routes, typical schedules and the used mode of transport. Furthermore, we propose an approach to model the probability of an event like missing a train as a function of time. This allows to optimize the expected commuting time based solely on the commuters motion history. Suitability of the approach is demonstrated in a real world application based on a dataset comprising six weeks of GPS tracks.