A Data-Driven Crowdsensing Framework for Parking Violation Detection

Parking violation is a common urban problem in major cities all over the world. Traditional approaches for detecting parking violations mainly rely on fixed deployed sensors and enforcement agencies, which suffer from high deployment costs and limited coverage. With the rapid development of mobile networks, Mobile CrowdSensing (MCS) has been an effective sensing paradigm. The crowdsensing data can help predict the future parking violation distribution, and the prediction results can provide guidance for user scheduling, i.e., sending the mobile users to patrol areas where many parking violation events may occur. Inspired by this idea, we propose a comprehensive data-driven crowdsensing framework, which incorporates the nested design of a generative model for spatial-temporal data and a user scheduling model. The generative model extracts parking violation hotspots via a data completion module and violation prediction module. Since crowdsensing data is usually temporally sparse and unevenly distributed, a data completion module is proposed to infer the missing statistics in unsensed areas. The violation prediction module then predicts the parking violation distribution. Given the predicted results, the deep reinforcement learning-based user scheduling model coordinates users to visit hotspots for violation detection. Iteratively, the newly collected data can be used to predict the future violation distribution. Finally, we conduct extensive simulations based on two real-world datasets from two large urban cities. The simulation verifies the prediction accuracy and scheduling effectiveness of the proposed framework compared with the baselines.