Monitoring and mining GPS traces in transit space

Users of mass transit systems such as those of buses and trains normally rely on accurate route maps, stop locations, and service schedules when traveling. If the route map, service schedule, or stop location has errors it can reduce the transit agency’s ridership. In this paper, the problem of deriving transit systems by mining raw GPS data is studied. Specifically, we propose and evaluate novel classification features with spatial and temporal clustering techniques that derive bus stop locations, route geometries, and service schedules from GPS data. Subsequently, manual and expensive field visits to record and annotate the initial or updated route geometries, transit stop locations, or service schedules is no longer required by transit agencies. This facilitates a massive reduction in cost for transit agencies. The effectiveness of the proposed algorithms is validated on the third largest public transit system in the United States. General terms Algorithms, Design, Experimentation, Performance