Inferring Taxi Status Using GPS Trajectories
This work addresses urban transportation and planning by providing status information from taxi GPS data, but it is incremental as it builds on existing trajectory analysis methods.
The paper tackles the problem of inferring taxi statuses (occupied, non-occupied, parked) from GPS trajectories to enable urban computing for transportation and planning. It proposes a method using feature extraction, parking detection, and a two-phase inference model, evaluated on a dataset from 600 taxis, showing advantages over baselines.
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.