Fast and scalable big data trajectory clustering for understanding urban mobility

Abstract

Clustering of large-scale vehicle trajectories is an important aspect for understanding urban traffic patterns, particularly for optimizing public transport routes and frequencies and improving the decisions made by authorities. Existing trajectory clustering schemes are not well suited to large numbers of trajectories in dense city road networks due to the difficulty in finding a representative distance measure between trajectories that can scale to very large datasets. In this paper, we propose a novel Dijkstra-based dynamic time warping distance measure, trajDTW between two trajectories, which is suitable for large numbers of overlapping trajectories in a dense road network as found in ma..