| Issue |
ITM Web Conf.
Volume 11, 2017
2017 International Conference on Information Science and Technology (IST 2017)
|
|
|---|---|---|
| Article Number | 10003 | |
| Number of page(s) | 8 | |
| Section | Session X: Big Data Processing and Management | |
| DOI | https://doi.org/10.1051/itmconf/20171110003 | |
| Published online | 23 May 2017 | |
A Driving Violation Index Modelling Study at Intersections in Urban Precincts Using Big Data
1 School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China
2 Traffic Engineering and Technology Research Institute, Tianjin Public Security Traffic Management Bureau, Tianjin, 300040, China
a Corresponding author: jiayan2010@sina.com
Driving violations at intersections are profoundly prevalent in urban precinct in China. These violations, including failing to stop for a red traffic light or arrow, driving across single continuous dividing line, disobeying traffic lane markings, and illegal parking, have become a major issue not only impacting the overall safety of the cities, but also causing traffic delays and accidents. This article focuses on the behaviors of the driving violations at intersections, and investigates what the influencing factors and characteristics are. A model of Driving Violation Index (DVI) for these driving violations at intersections has been developed by utilizing the Big Data approach. Based on the actual traffic data from the intersections in the city of Tianjin, a relationship has been fitted and established between the driving violations and the influencing factors, and further yielded the model of calculating the related DVI. This research attempts to provide a mechanism to forecast the driving violations at intersections, and explore new strategies for managing the security and safety of the intersections.
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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