Histogram-based Feature Extraction for GPS Trajectory Clustering

Clustering trajectories from GPS data is a crucial task for developing applications in intelligent transportation systems. Most existing approaches perform clustering on raw data consisting of series of GPS positions of moving objects over time. Such approaches are not suitable for classifying moving behaviours of vehicles, e.g., how to distinguish between a trajectory of a taxi and a trajectory of a private car. In this paper, we focus on the problem of clustering trajectories of vehicles having the same moving behaviours. Our approach is based on histogram-based feature extraction to model moving behaviours of objects and utilizes traditional clustering algorithms to group trajectories. We perform experiments on real datasets and obtain better results than existing approaches.