Urban Population Migration Pattern Mining Based on Taxi Trajectories

Understanding urban population migration patterns is very helpful for urban operation and management, in-cluding the traffic forecasting, epidemic prevention, com-mercial resource allocation, emergency response and fu-ture urban planning. The large taxi fleet equipped with GPS comprises ubiquitous mobile probes in urban ar-eas, and their trajectories reveal interesting phenomena in the city. We investigate the urban population migra-tion pattern mining based on taxi trajectories, including 1) the hotzone identification and 2) the principal Origin-Destination 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 algo-rithms are evaluated on a Beijing taxi trajectory dataset generated by 8,000 taxicabs in May 2009. The result-s show interesting insights of the time-resolved results, which is consistent with semantic interpretations