STL: Online Detection of Taxi Trajectory Anomaly Based on Spatial-Temporal Laws

Aiming to promote the standardization of taxi services and protect the interests of passengers, many methods have been proposed to detect taxi trajectory anomaly based on collected large-scale taxi traces. However, most existing methods usually employ a counting-based policy to differentiate normal trajectories from anomalous ones, which may give rise to high false positives. In this paper, we propose an online detection method, named Spatial-Temporal Laws (STL). The basic idea of STL is that, given the displacement from the source point to the current point of a testing trajectory, if the current point is normal, either its driving distance or driving time should lie in a normal range. STL learns the two ranges from historical trajectories by defining two spatial-temporal models: one characterizing the relationship between displacement and driving distance, and another depicting the relationship between displacement and driving time. Consequently, STL is more precise compared with the counting-based methods, greatly reducing the number of false positives. Based on large-scale real-world taxi traces, STL is evaluated through a series of experiments which demonstrate its effectiveness and performance.