Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (4): 1336-1345.doi: 10.13229/j.cnki.jdxbgxb.20230689

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Dynamic estimation of operational risk of tunnel traffic flow based on spatial-temporal Transformer network

Zhen-jiang LI1(),Li WAN1,Shi-rui ZHOU2,Chu-qing TAO1,Wei WEI2()   

  1. 1.Tunnel and Underground Engineering Design Branch，Shandong Provincial Communications Planning and Design Institute Group Company Limited，Jinan 250000，China
    2.College of Transportation，Jilin University，Changchun 130022，China
  • Received:2023-06-01 Online:2025-04-01 Published:2025-06-19
  • Contact: Wei WEI E-mail:jnlzj@163.com;weiwei@jlu.edu.cn

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Abstract:

To promptly detect， evaluate， and address potential traffic risks in highway tunnels， ensuring the safe and efficient operation of tunnels， a dynamic estimation method for tunnel operational risk states was proposed based on spatial-temporal Transformer network. Tunnel traffic flow holographic detection and key cross-section aggregation information as inputs was utilized， the spatial convolution and temporal LSTM was employed by proposed model for unsupervised extraction of spatiotemporal distribution features of different tunnel traffic operational states. Through extensive sample training of Transformer network layer parameters，it aims to capture the distribution and variances of tunnel traffic states in a high-dimensional risk feature space. This facilitates the estimation of operational risk of tunnel traffic flow. The effectiveness of the proposed method is verified by using real tunnel traffic detection data， and the accuracy of tunnel operation risk estimation is about 96%.

Key words: traffic transport engineering, tunnel traffic safety, dynamic evaluation, deep learning, time series convolutional network

CLC Number: 

  • U458

Fig.1

Tunnel safety evaluation framework based on spatial-temporal Transformer network"

Fig.2

CNN-space feature convolution layer structure"

Fig.3

LSTM-sequence feature extraction layer structure"

Fig.4

Transformer risk evaluation layer structure"

Fig.5

Simulation diagram of tunnel risk state"

Table 1

Other parameters setting of deep sequential convolutional network"

相关参数设定值
激活函数σRelu
网络学习率0.001
最大训练次数Ttrain30 000
最小批训练尺寸10
式（13）中分类数3

Table 2

Tunnel risk state evaluation result confusion matrix"

真实类别评估结果
正例反例
正例真正例TP假反例FN
反例假正例FP真反例TN

Fig.6

Confusion matrix of tunnel risk state estimation results under different data utilization conditions"

Table 3

Comparison of tunnel risk state estimation results under different data utilization conditions"

模型精确率召回率F1分数准确率/%
全域数据+时空Transformer0.941 30.931 50.937 793.12
断面数据+时空Transformer0.910 20.898 10.904 290.37
本文方法0.960 30.963 20.964 196.18

Fig.7

Confusion matrix of different tunnel risk state estimation methods（Holographic detection data）"

Table 4

Comparison of tunnel risk state estimation results of different methods（Holographic detection data）"

模型精确率召回率F1分数准确率/%
SVM0.773 20.763 10.771 577.32
XGBoost0.738 70.753 60.752 275.57
CNN+Transformer0.879 80.858 10.868 887.02
LSTM+Transformer0.895 20.905 30.892 490.24
本文方法（全域数据）0.941 30.931 50.937 793.12

Table 5

Comparison of tunnel risk state estimation results of different methods（Key cross-section aggregation data）"

模 型精确率召回率F1分数准确率/%
贝叶斯分类0.798 20.815 40.806 780.57
随机森林0.803 70.821 40.815 481.37
XGBoost0.849 80.868 10.858 886.02
LSTM+Softmax0.836 10.849 80.833 784.24

本文方法

（断面集计数据）

0.910 20.898 10.904 290.37
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