LADD: A Length-Adaptive Approach to Detecting Taxi Anomalous Detours

This paper focuses on the practical problem of detecting taxi anomalous detours. In this paper, we propose a length-adaptive detection approach called LADD to detect if a taxi conducted an anomalous detour by investigating the taxi journey's trajectory. We employ a similarity-based detection strategy, which takes trajectory length into consideration. Our approach can deal with both long and short trajectories by applying weighted similarity on trajectories and using trajectory concatenation or clipping. For quantitative performance comparison, we propose a performance metric, and compare our approach with the state-of-the-art methods on a real-world dataset. The results verify the superior performance of LADD on taxi detour detection.