  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. The government highly relies on active human efforts to detect
illegal parking events. However, such an approach is extremely ineffective to
cover a large city since the police have to patrol over the entire city roads.
  The massive and high-quality sharing bike trajectories from Mobike offer us a
unique opportunity to design a ubiquitous illegal parking detection approach,
as most of the illegal parking events happen at curbsides and have significant
impact on the bike users. The detection result can guide the patrol schedule,
i.e. send the patrol policemen to the region with higher illegal parking risks,
and further improve the patrol efficiency. Inspired by this idea, three main
components are employed in the proposed framework: 1)~{\em trajectory
pre-processing}, which filters outlier GPS points, performs map-matching, and
builds trajectory indexes; 2)~{\em 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; and 3)~{\em patrol scheduling}, which leverages the detection result as
reference context, and models the scheduling task as a multi-agent
reinforcement learning problem to guide the patrol police. Finally, extensive
experiments are presented to validate the effectiveness of illegal parking
detection, as well as the improvement of patrol efficiency.
