  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.

Computers and Society