iTV：利用车辆轨迹和道路环境数据推断交通违章倾向位置
iTV: Inferring Traffic Violation-Prone Locations with Vehicle Trajectories and Road Environment Data
摘要：
违章停车、违章转弯、超速等交通违法行为已成为城市交通系统面临的最大挑战之一，带来了交通拥堵、车辆事故、停车难等潜在风险。为了最大限度地提高旨在减少交通违法行为的交通执法策略的效用和有效性，城市当局必须推断城市中的交通违法易发地点。因此，我们提出了一个低成本的，全面的，动态的框架来推断交通违法的易发地点在城市大规模的车辆轨迹数据和道路环境数据的基础上。首先，通过地图匹配算法对轨迹数据进行归一化，提取关键驾驶行为，车辆的转弯行为、停车行为和速度。其次，我们恢复驾驶行为的时空背景，以获得相应的交通限制，如禁止停车，禁止转弯，和速度限制。将交通限制与驾驶行为进行匹配后，得到交通违章分布。最后，我们提取的时空模式的交通违法，并建立一个可视化系统来展示推断的交通违法的易发地点。为了评估所提出的方法的有效性，我们进行了广泛的研究，大规模的，现实世界中的车辆GPS轨迹收集从中国两个城市，分别。评价结果表明，该框架能够有效地推断出交通违法的易发地点，为交通执法策略提供全面的决策支持。
英文摘要：
Traffic violations like illegal parking, illegal turning, and speeding have become one of the greatest challenges in urban transportation systems, bringing potential risks of traffic congestions, vehicle accidents, and parking difficulties. To maximize the utility and effectiveness of the traffic enforcement strategies aiming at reducing traffic violations, it is essential for urban authorities to infer the traffic violation-prone locations in the city. Therefore, we propose a low-cost, comprehensive, and dynamic framework to infer traffic violation-prone locations in cities based on the large-scale vehicle trajectory data and road environment data. Firstly, we normalize the trajectory data by map matching algorithms and extract key driving behaviors, i.e., turning behaviors, parking behaviors, and speeds of vehicles. Secondly, we restore spatiotemporal contexts of driving behaviors to get corresponding traffic restrictions such as no parking, no turning, and speed restrictions. After matching the traffic restrictions with driving behaviors, we get the traffic violation distribution. Finally, we extract the spatiotemporal patterns of traffic violations, and build a visualization system to showcase the inferred traffic violation-prone locations. To evaluate the effectiveness of the proposed method, we conduct extensive studies on large-scale, real-world vehicle GPS trajectories collected from two Chinese cities, respectively. Evaluation results confirm that the proposed framework infers traffic violation-prone locations effectively and efficiently, providing comprehensive decision supports for traffic enforcement strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
