| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 02055 | |
| Number of page(s) | 9 | |
| Section | 3D Images Reconstruction and Virtual System | |
| DOI | https://doi.org/10.1051/matecconf/201823202055 | |
| Published online | 19 November 2018 | |
Illegal parking road recognition based on video detection equipment
1
Guizhou University Guizhou Provincial Key Laboratory of Public Big Data, Guizhou Guiyang, 550025, China
2
Guizhou University, School of Mathematics and Statistics, Guizhou Guiyang, 550025, China
3
Guiyang Public Security Traffic Administration Bureau, Science and Technology Department, Guizhou Guiyang, 550081, China
a Corresponding author: fan.xinyue@163.com
Based on the big data collected by the video detection equipment, the network topology table of the city level video detection equipment is constructed by using the time relation and the spatial position relation of the data. By using the steepest descent method and adaptive method, the travel confidence time randomness model is constructed, which can describe whether a traveler can finish his travel time on time. It overcomes the shortcomings of the existing travel time reliability calculation model, which is difficult to combine with the actual use of video detection equipment data, then examples analysis are followed. The results show that, for the data collected by the video detection device, the travel confidence time randomness model is more accurate than the existing models. It can describe the probability of the traveler arriving at the destination in a given time more accurately, which can be used to identify illegal parking road and provide a reliable basis for traffic management departments in traffic planning, dividing road network status and traffic situation prediction.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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