Robust inferences of travel paths from GPS trajectories
نویسندگان
چکیده
Monitoring and predicting traffic conditions is of utmost importance for reacting to emergency events in time and for real-time shortest travel-time path computations. Mobile sensors, such as GPS devices and smartphones, are useful for monitoring the urban traffic due to their large coverage and easy deployment. Many researchers have employed such sensed data to model and predict traffic conditions. In order 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 movement direction as a spatiallinear 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 the state-of-the-art methods in terms of both accuracy and computational cost.
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عنوان ژورنال:
- International Journal of Geographical Information Science
دوره 29 شماره
صفحات -
تاریخ انتشار 2015