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iTV: Inferring Traffic Violation-Prone Locations with Vehicle Trajectories and Road Environment Data by Jiang, Zhihan; Chen, Longbiao; Zhou, Binbin; Huang, Jinchun; Xie, Tianqi; Fan, Xiaoliang; Wang, Cheng is a scholarly article available to read on EtoBox.

What is iTV: Inferring Traffic Violation-Prone Locations with Vehicle Trajectories and Road Environment Data about?

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 sy

Author

Jiang, Zhihan; Chen, Longbiao; Zhou, Binbin; Huang, Jinchun; Xie, Tianqi; Fan, Xiaoliang; Wang, Cheng

Published

2020

Language

EN