LADD: A LengthAdaptive Approach to Detecting Taxi Anomalous Detours This paper focuses on the practical problem of detecting taxi anomalous detours. In this paper, we propose a lengthadaptive detection approach called LADD to detect if a taxi conducted an anomalous detour by investigating the taxi journeyu0027s trajectory. We employ a similaritybased 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 stateoftheart methods on a realworld dataset. The results verify the superior performance of LADD on taxi detour detection.

Comparative Study on Trajectory Outlier Detection Algorithms This paper explores, reviews, and evaluates the existing outlier trajectory detection using small, large and big databases. We divide existing solutions into two main categories: similaritybased, and clusteringbased approaches. The first category groups solutions employing distance and neighborhoods computation to derive local density estimates. The second category explores the correlation between the trajectory data by using clustering techniques. Solutions in each of these categories are explained, implemented and compared using different trajectory database sizes. Compared to stateoftheart survey papers, the contribution of this paper is in providing a deep analysis of all kinds of trajectory outlier detection methods. In this context, we can better understand the intuition, limitations, and benefits of the existing outlier trajectory detection algorithms. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.