题目：Learning to Route with Sparse Trajectory Sets
讲者：Prof. Bin Yang
Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality.
Bin Yang is an Associate Professor in Department of Computer Science at Aalborg University, Denmark. He was at Aarhus University, Denmark, during 2011–2014 and at Max-Planck-Institut für Informatik, Germany, during 2010–2011. He received the Ph.D. degree in computer science from Fudan University in 2010. His research interests include data management and data analytics. Bin received the Distinguished Scholar award in 2018, given by the Technical Faculty of IT and Design, Aalborg University, the best paper award at MDM 2013, and the best demo award at MDM 2013. He is an IEEE senior member. He has served on program committees and as an invited reviewer for several international conferences and journals, including ICDE, IJCAI, TKDE, The VLDB Journal, and ACM Computing Surveys.