Robust inferences of travel paths from GPS trajectories

Monitoring and predicting traffic conditions are of utmost importance in reacting to emergency events in time and for computing the real-time shortest travel-time path. Mobile sensors, such as GPS devices and smartphones, are useful for monitoring urban traffic due to their large coverage area and ease of deployment. Many researchers have employed such sensed data to model and predict traffic conditions. To do so, we first have to address the problem of associating GPS trajectories with the road network in a robust manner. Existing methods rely on point-by-point matching to map individual GPS points to a road segment. However, GPS data is imprecise due to noise in GPS signals. GPS coordinates can have errors of several meters and, therefore, direct mapping of individual points is error prone. Acknowledging that every GPS point is potentially noisy, we propose a radically different approach to overcome inaccuracy in GPS data. Instead of focusing on a point-by-point approach, our proposed method considers the set of relevant GPS points in a trajectory that can be mapped together to a road segment. This clustering approach gives us a macroscopic view of the GPS trajectories even under very noisy conditions. Our method clusters points based on the direction of movement as a spatial-linear cluster, ranks the possible route segments in the graph for each group, and searches for the best combination of segments as the overall path for the given set of GPS points. Through extensive experiments on both synthetic and real datasets, we demonstrate that, even with highly noisy GPS measurements, our proposed algorithm outperforms state-of-the-art methods in terms of both accuracy and computational cost.