报告题目：Trajectory Data Management: From Sparse Points to Smart Mobility

报告时间：2026年7月16日下午2:30

报告地点： 综合楼204

报告人：田伟

报告内容：

With the rapid development of satellite positioning, 5G communication, and Internet of Things (IoT) technologies, there is an explosive growth in trajectory data. A trajectory is commonly in the form of a sequence of spatio-temporal GPS points, which describes an object’s movement over time. However, in real world, most collected trajectories are often sparse with low-sampling rates, noisy and structurally incomplete, which leads to severe performance degradation in intelligence transportation systems.

This talk will present a set of lightweight algorithms to achieve high-quality, efficient, and scalable solutions for map-matching, trajectory recovery and route planning. Specifically, I will introduce TRMMA and MMA for accurate trajectory recovery and map matching, respectively. TRMMA and MMA mitigate the issues of sparse trajectories and improve data quality, further support the route planning task. Then, I will present DRPK, an effective and efficient route planning method that achieves state-of-the-art performance via a series of novel algorithmic designs.

报告人简介：

Dr. Wei Tian obtained the PhD degree in computer science from The Hong Kong Polytechnic University in 2026, and the BEng degree in computer science and technology from Sichuan University in 2017. His research interests lie at the spatial databases, spatio-temporal data management and data mining, urban computing. He has published many high-quality papers to top-tier venues, such as VLDB, ICDE, KDD. His collaboration includes RMIT University in Australia and DAMO Academy of Alibaba. He also serves as the Program Committee member of several conferences, such as SIGSPATIAL.