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.

TPM: A GPSbased Trajectory Pattern Mining System With the development of big data and artificial intelligence, the technology of urban computing becomes more mature and widely used. In urban computing, using GPSbased trajectory data to discover urban dense areas, extract similar urban trajectories, predict urban traffic, and solve traffic congestion problems are all important issues. This paper presents a GPSbased trajectory pattern mining system called TPM. Firstly, the TPM can mine urban dense areas via clustering the spatialtemporal data, and automatically generate trajectories after the timing trajectory identification. Mainly, we propose a method for trajectory similarity matching, and similar trajectories can be extracted via the trajectory similarity matching in this system. The TPM can be applied to the trajectory system equipped with the GPS device, such as the vehicle trajectory, the bicycle trajectory, the electronic bracelet trajectory, etc., to provide services for traffic navigation and journey recommendation. Meantime, the system can provide support in the decision for urban resource allocation, urban functional region identification, traffic congestion and so on.