Detecting vehicle illegal parking events using sharing bikes' trajectories

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect i l - legal parking events rely highly on active human efforts. However, these approaches are extremely ineffective to cover a large city. The massive and high quality sharing bike trajectories from Mobike offer us with a unique opportunity to design a ubiquitous illegal parking detection system, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. Two main components are employed in the proposed illegal parking detection system: 1) trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds trajectory indexes; and 2) illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events. The system is deployed on the cloud, and used by Mobike internally. Finally, extensive experiments and many insightful case studies are presented.