Urban Population Migration Pattern Mining Based on Taxi Trajectories

Understanding urban population migration patterns is very helpful for urban operation and management, including the traffic forecasting, epidemic prevention, commercial resource allocation, emergency response and future urban planning. The large taxi fleet equipped with GPS comprises ubiquitous mobile probes in urban areas, and their trajectories reveal interesting phenomena in the city. We investigate the urban population migration pattern mining based on taxi trajectories, including 1) the hotzone identification and 2) the principal OriginDestination traffic flow (OD pair) extraction. We show how to apply state-of-art point clustering algorithms to address these two tasks. The performance of the algorithms are evaluated on a Beijing taxi trajectory dataset generated by 8,000 taxicabs in May 2009. The results show interesting insights of the time-resolved results, which is consistent with semantic interpretations.