In street-based mobility mining, traffic volume estimation receives increasing attention as it provides important applications such as emergency support systems, quality-of-service evaluation and billboard placement. In many real world scenarios, empirical measurements are usually sparse due to some constraints. On the other hand, pedestrians generally show some movement preferences, especially in closed environments, e.g., train stations. We propose a Gaussian process regression based method for traffic volume estimation, which incorporates topological information and prior knowledge on preferred trajectories with a trajectory pattern kernel. Our approach also enables effectively finding most informative sensor placements. We evaluate our method with synthetic German train station pedestr ian data and real-world episodic movement data from the zoo of Duisburg. The empirical analysis demonstrates that incorporating trajectory patterns can largely improve the traffic prediction accuracy, especially when traffic networks are sparsely monitored
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.