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.
翻译:非法车辆泊车是世界主要城市面临的一个常见的城市问题,因为它引起交通堵塞,导致空气污染和交通事故。政府高度依赖人的积极努力来发现非法泊车事件。然而,由于警察必须在整个城市公路上巡逻,这种方法在覆盖一个大城市方面极为无效。莫比克的大规模和高质量的共用自行车轨迹为我们提供了一个独特的机会来设计一种普遍的非法违章泊车检查方法,因为大多数非法泊车事件发生在路边,对自行车使用者有重大影响。探测结果可以指导巡逻时间表,即派巡逻警察到非法泊车风险较高的地区,并进一步提高巡逻效率。受这一想法的启发,拟议框架使用了三个主要组成部分:1 ⁇ em轨迹预处理},它过滤超过全球定位系统的点,进行地图匹配,建立轨迹索引;2 ⁇ em非法泊车检查},它模拟正常的轨迹,从评估轨迹中提取特征,利用分布巡警巡警到区域巡车效率更高的区域,并使用分配测试工具来发现非法巡车的巡车进度表。最后,这是一个基于分配测试的巡车进度表的进度表, 和跨路路路路段的定位,这是一个测试工具,用来查找定位,用来发现非法巡航程。