Clustering Object Trajectories for Intersection Traffic Analysis

Vehicle and pedestrian traffic at a traffic intersection provide crucial information about the performance of the intersection for safety and throughput. It is possible to discover patterns and outliers on this data by applying data analytics. In this paper, we present a novel clustering algorithm for trajectories that use a new distance measure and a two-level hierarchical clustering approach based on geometric properties of the trajectories and spectral clustering. Trajectory data is augmented with signal phasing and timing information, which gives new insights to the trajectory data. We demonstrate the procedure on a real-life intersection where the prominent patterns for traffic movement are found, and the anomalous trajectories are extracted.