2012/05/20 by Yin Zhu, Zhu, Yin, Yu Zheng +9
Computer Science · #68T20 #Artificial Intelligence (cs.AI) #Databases (cs.DB) #FOS: Computer and information sciences #H.2.8 #acm:68T20 #cs.AI #cs.DB #msc:68T20
paper · pdf · doi:10.48550/arxiv.1205.4378
arxiv created 2012/06/18 · arxiv updated 2012/06/19
In this paper, we infer the statuses of a taxi, consisting of occupied, non-occupied and parked, in terms of its GPS trajectory. The status information can enable urban computing for improving a city's transportation systems and land use planning. In our solution, we first identify and extract a set of effective features incorporating the knowledge of a single trajectory, historical trajectories and geographic data like road network. Second, a parking status detection algorithm is devised to find parking places (from a given trajectory), dividing a trajectory into segments (i.e., sub-trajectories). Third, we propose a two-phase inference model to learn the status (occupied or non-occupied) of each point from a taxi segment. This model first uses the identified features to train a local probabilistic classifier and then carries out a Hidden Semi-Markov Model (HSMM) for globally considering long term travel patterns. We evaluated our method with a large-scale real-world trajectory dataset generated by 600 taxis, showing the advantages of our method over baselines.