Prediction method of geographical and spatial distribution of traffic accidents based on traffic flow big data

被引：0
|
作者：
Liu Y. [1 ]
Zhang Z.A. [2 ]
Shang Z.L. [3 ]
Wang Z. [4 ]
机构：
[1] Cloud Computing and Big Data Institute of Henan University of Economics and Law, Henan, Zhengzhou
[2] The University of New South Wales, Sydney, 2032, NSW
[3] Engineering Training Center, Zhengzhou University of Light Industry, Zhengzhou
[4] College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou
来源：
Advances in Transportation Studies | 2023年 / 2卷 / Special issue期
关键词：
big data of traffic flow; geographical spatial distribution; kalman filtering; nearest neighbor; traffic accident;
D O I：
10.53136/979122180834610
中图分类号：
学科分类号：
摘要：
Accurate prediction of the geographical spatial distribution location of traffic accidents can provide drivers with richer traffic geographic information, thereby reducing the rate of traffic accidents. However, the existing methods for predicting the geographical spatial distribution location of traffic accidents have the problems of large error and long time. Therefore, this paper proposes a method for predicting the geographical spatial distribution location of traffic accidents based on the traffic flow gig data. First, collect traffic flow big data during the accident through highway detectors. Secondly, the collected data is processed for exception deletion, missing completion and geocode conversion. Finally, according to the geographical spatial distribution characteristics, the geographical spatial distribution of traffic accidents is predicted through Kalman filter. The experimental results show that this method predicts the geographical spatial distribution of traffic accidents based on actual test results, with good prediction effect and high prediction efficiency. © 2023, Aracne Editrice. All rights reserved.
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收藏
页码：113 / 124
页数：11
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