  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.
