Mining Urban Trajectories

现代传感技术和大规模计算基础设施的进步产生了大量的轨迹数据。这些城市轨迹数据包含了丰富的运动对象信息，包括人、车等，在道路网络重建、智能交通管理、空气质量控制等方面有着广泛的应用。虽然政府、工业公司和组织已经意识到这些数据的巨大潜力，但它们的复杂性带来了难以解决的独特挑战。******拟建的研究项目旨在解决挖掘城市轨迹数据所带来的挑战，并为以下三个研究问题提供答案：如何从轨迹中理解物体的移动模式？如何融合异构轨迹，解决数据稀疏性问题？如何从城市轨迹中检测异常？******本研究的结果将推动最先进的轨迹数据挖掘技术，为从不同角度理解人们的移动行为提供新的见解。此外，通过研究成果带来的一系列新的应用和服务，将为包括政府、企业和最终用户在内的各方带来显著的利益。

Advances in modern sensing technologies and large scale computing infrastructures have generated a huge amount of trajectory data. These urban trajectory data contain rich information of moving objects including people, vehicle, etc., and foster a wide variety of applications, such as road network reconstruction, intelligent traffic management, and air quality control. While governments, industrial companies, and organizations have realized the tremendous potential of these data, their complex nature presents unique challenges that cannot be easily solved.******The objective of the proposed research program is to address the challenges arising from mining urban trajectory data, and provides answers to the following three research questions: How to understand objects' mobility patterns from their trajectories? How to fuse the heterogeneous trajectories, and solve the data sparsity problem? How to detect anomalies from urban trajectories?******The results of this research will advance the state-of-the-art technique in trajectory data mining, providing new insights on understanding people's moving behaviors from various perspectives. Besides, it will bring significant benefit to different parties, including governments, businesses, and end users, through a series of new applications and services enabled by the research results.