Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (6): 1337-1343.doi: 10.13229/j.cnki.jdxbgxb20210697

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Traffic safety prediction of urban underpass tunnel vehicles based on edge intelligence

Hai-xiao WANG1(),Yong-xiang LI1(),Xu DING1,Bao-hua ZHANG2   

  1. 1.College of Energy & Transportation Engineering，Inner Mongolia Agricultural University，Hohhot 010018，China
    2.School of Information Engineering，Inner Mongolia University of Science and Technology，Baotou 014010，China
  • Received:2021-07-29 Online:2022-06-01 Published:2022-06-02
  • Contact: Yong-xiang LI E-mail:wanghx@imau.edu.cn;lyxiang@imau.edu.cn

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Abstract:

When the traditional method is used to predict the traffic safety of urban underpass tunnels， the influencing factors of vehicle safety are not fully analyzed， and the prediction accuracy of traffic safety is low and the prediction results are unstable.In this paper，a safety prediction method based on edge intelligence is proposed.Firstly， an edge intelligent deep learning model is constructed to acquire and process driving data and analyze the influencing factors of traffic safety. Then， according to the analysis results， the traffic conflicts in urban underpass tunnels under different conditions of traffic flow are predicted.Finally， the predictive performance of this method is compared with other traditional machine learning by simulation.The results show that this method can predict the risk of traffic accidents， and the accuracy of traffic safety prediction is more than 90%， which is higher than other traditional methods.

Key words: edge intelligence, urban underpass tunnels, vehicle influencing factors, safety prediction

CLC Number: 

  • U491.1

Fig.1

Edge depth learning model"

Fig.2

Predicted value and measured value of traffic conflict in different traffic volume"

Fig.3

Comparison of prediction accuracy of the three prediction models"

Fig.4

ROC comparison of the three prediction models"

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