Mining Urban Trajectories

挖掘城市轨迹

• 批准号: RGPIN-2018-06641
• 负责人: Liu, Yang
• 金额: $ 1.68万
• 依托单位: Wilfrid Laurier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714375
• 关键词: Mining; Urban; Trajectories

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.

现代传感技术和大规模计算基础设施的进步已经产生了大量的轨迹数据。这些城市轨迹数据包含了人、车等移动对象的丰富信息，促进路网改造、智能交通管理、空气质量控制等广泛应用。虽然政府、工业企业和组织已经意识到这些数据的巨大潜力，但其复杂性带来了难以解决的独特挑战。 拟议的研究计划的目标是解决挖掘城市轨迹数据所带来的挑战，并提供了以下三个研究问题的答案：如何理解对象的移动模式，从他们的轨迹？如何融合异构轨迹，解决数据稀疏问题？如何从城市轨迹中发现异常？ 这项研究的结果将推进轨迹数据挖掘的最新技术，从多个角度了解人们的移动行为提供新的见解。此外，它将通过研究成果实现的一系列新应用和服务，为包括政府，企业和最终用户在内的各方带来重大利益。