iTV: Inferring Traffic Violation-Prone Locations With Vehicle Trajectories and Road Environment Data

@article{Jiang2021iTVIT,
  title={iTV: Inferring Traffic Violation-Prone Locations With Vehicle Trajectories and Road Environment Data},
  author={Zhihan Jiang and Longbiao Chen and Binbin Zhou and Jinchun Huang and Tianqi Xie and Xiaoliang Fan and Cheng Wang},
  journal={IEEE Systems Journal},
  year={2021},
  volume={15},
  pages={3913-3924}
}
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 cities. Therefore, we propose a low-cost… 
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