A Scalable Framework for Clustering Vehicle Trajectories in a Dense Road Network

Cluster analysis is a fundamental challenge in trajectory mining. However, existing trajectory clustering algorithms are not well suited to large numbers of trajectories in a city road network because of inadequate distance measures between two trajectories. In this paper we propose a novel Dijkstra based Dynamic Time Warping (DTW) distance measure, trajDTW between two trajectories, which is suitable for large numbers of overlapping trajectories in a dense road network. We show the superiority of trajDTW over previously proposed distance measures Dissimilarity with Length (DSL) and Hausdorff distance for point sets using a few sample trajectories on a road network. We then show how our sampling based clustering algorithm clusiVAT can suggest the number of clusters, and identify and visualize the trajectories belonging to each cluster. We also detect anomalous trajectories in a given dataset using clusiVAT. Experimental results on a large scale T-Drive taxi trajectory dataset consisting of 43,405 trajectories on a road network having 100 nodes and 141 edges reveals the presence of 12 clusters having an average of 2,029 trajectories each. We compare the trajectory clusters obtained using the clusiVAT algorithm employing trajDTW distance measure with those obtained using the NETSCAN trajectory clustering method proposed in the literature. Furthermore, we identify the top 100 anomalies corresponding to a few vehicles taking unusually warped paths for their commute. These anomalous trajectories have their maximum traffic density in geographically distinct sections of the road network.