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

Previous Articles    

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

RICH HTML

 8   

PDF (PC)

233

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"

1 蔡延光, 陈骋逵, 蔡颢, 等. 基于视频信息的城市路段交通安全状态评估方法研究[J]. 计算机应用研究, 2019, 36(1): 73-76.
Cai Yan-guang, Chen Cheng-kui, Cai Hao, et al. Safety state evaluation method of urban road traffic based on video information[J]. Application Research of Computers, 2019, 36(1): 73-76.
2 Wang Hai-xiao, Liu Fang, Tang Jin-jun. Exploring intra-urban travel mobility using large-scale taxi global positioning system trajectories[J]. International Journal of Vehicle Structures and Systems, 2018, 10(2): 150-159.
3 赵海涛, 程慧玲, 丁仪, 等. 基于深度学习的车联边缘网络交通事故风险预测算法研究[J]. 电子与信息学报, 2020, 42(1): 50-57.
Zhao Hai-tao, Cheng Hui-ling, Ding Yi, et al. Research on traffic accident risk prediction algorithm of edge internet of vehicles based on deep learning[J]. Journal of Electronics & Information Technology, 2020, 42(1): 50-57.
4 陈良, 史志才, 张翔, 等. 基于粒子群优化人工神经网络的临界行车安全距离预测[J]. 汽车技术, 2020(3): 1-4.
Chen Liang, Shi Zhi-cai, Zhang Xiang, et al. Prediction and simulation of critical driving safety distance based on PSO-ANN[J]. Automobile Technology, 2020(3): 1-4.
5 唐敏, 王东强, 曾鑫钰. 汽车碰撞预警主动安全预测方法[J]. 计算机科学, 2020, 47(4): 318-322.
Tang Min, Wang Dong-qiang, Zeng Xin-yu. Active safety prediction method for automobile collision warning[J]. Computer Science, 2020, 47(4): 318-322.
6 李志慧, 孙雅倩, 陶鹏飞, 等.交通事故后的交通运行风险状态等级预测方法[J]. 吉林大学学报: 工学版, 2022, 52(1): 127-135.
Li Zhi-hui, Sun Ya-qian, Tao Peng-fei, et al. Prediction method of traffic operation risk level after traffic accident[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(1): 127-135.
7 吴立新, 程国柱, 秦丽辉, 等. 寒区冰雪期城市道路交通冲突与事故预测[J]. 交通运输系统工程与信息, 2016, 16(6): 216-221.
Wu Li-xin, Cheng Guo-zhu, Qin Li-hui, et al. Urban road traffic conflict and accident prediction during the period of ice and snow in cold region[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(6): 216-221.
8 王蕾, 邱锋, 夏永旭, 等. 基于道路环境因素的公路隧道交通事故预测[J]. 隧道建设, 2019, 39(8): 1301-1307.
Wang Lei, Qiu Feng, Xia Yong-xu, et al. Traffic accident prediction of highway tunnel based on road environmental factor[J]. Tunnel Construction, 2019, 39(8): 1301-1307.
9 孙志刚, 王国涛, 蒋爱平, 等. 基于信息融合技术的行车安全监测系统[J]. 计算机科学, 2020, 47(): 642-650, 661.
Sun Zhi-gang, Wang Guo-tao, Jiang Ai-ping, et al. Monitoring system of traffic safety based on information fusion technology[J]. Computer Science, 2020, 47(Sup.2): 642-650, 661.
10 陈计伟, 史志才, 刘瑾, 等. 道路交通行车安全预警算法研究[J]. 电子测量技术, 2019, 42(5): 6-10.
Chen Ji-wei, Shi Zhi-cai, Liu Jin, et al. Research on early warning algorithm of driving safety about road traffic[J]. Electronic Measurement Technology, 2019, 42(5): 6-10.
11 赵慧龙, 宋战平, 王军保. 某矿山法施工隧道下穿公路数值模拟分析[J]. 现代隧道技术, 2019, 56(): 340-346.
Zhao Hui-long, Song Zhan-ping, Wang Jun-bao. Numerical simulation and analysis of a mined tunnel crossing under a highway based on MIDAS-GTS[J]. Modern Tunnelling Technology, 2019, 56(Sup.2): 340-346.
12 方博文, 张晓东, 陈敬义, 等. 基于双目视觉的行车中障碍距离检测方法研究[J]. 机械设计与制造, 2019(4): 94-98.
Fang Bo-wen, Zhang Xiao-dong, Chen Jing-yi, et al. Research on detecting method of obstacle distance in driving based on binocular vision[J]. Machinery Design & Manufacture, 2019(4): 94-98.
13 王露, 刘玉雯, 陈红. 侧风下峡谷桥隧连接段汽车的行驶特性[J].吉林大学学报: 工学版, 2019, 49(3): 736-748.
Wang Lu, Liu Yu-wen, Chen Hong. Cross-wind environment vehicle driving feature at canyon bridge and tunnel connection segment[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(3): 736-748.
14 朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33.
Zhu Shun-ying, Jiang Ruo-xi, Wang Hong, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway and Transport, 2020, 33(2): 15-33.
15 王乾, 蒋树屏, 刘海龙, 等. 城市道路隧道交通事故特征分析[J]. 地下空间与工程学报, 2019, 15(): 460-469.
Wang Qian, Jiang Shu-ping, Liu Hai-long, et al. Analysis on traffic accident characteristics of urban road tunnels[J]. Chinese Journal of Underground Space and Engineering, 2019, 15(Sup.1): 460-469.
16 朱彤, 吴玲, 胡月琦, 等. 基于因子模型的高速公路特长隧道驾驶人心理负荷特性研究[J]. 中国公路学报, 2018, 31(11): 165-175.
Zhu Tong, Wu Ling, Hu Yue-qi, et al. Research on characteristics of drivers' mental workload in extra-long expressway tunnels based on the factor model[J]. China Journal of Highway and Transport, 2018, 31(11): 165-175.
[1] Shi-jun YANG,Yu-long PEI,Heng-yan PAN,Guo-zhu CHENG,Wen-hui ZHANG. Characteristics analysising and prediction of dwelling time of urban bus [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2031-2039.
[2] Wen-long TENG,Bing-hu CONG,Yun-kun SHANG,Yu-chen ZHANG,Tian BAI. Modeling of building energy consumption prediction based on MEA⁃BP neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1857-1865.
[3] Bo PENG,Yuan-yuan ZHANG,Yu-ting WANG,Ju TANG,Ji-ming XIE. Automatic traffic state recognition from videos based on auto⁃encoder and classifiers [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 886-892.
[4] Cai-hua ZHU,Xiao-li SUN,Yan LI. Forecast of urban public bicycle traffic demand by station classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 531-540.
[5] Yu-lin CHANG,Cai-hong YUAN,Chao SUN,Peng ZHANG. Calculation method for actual impedance of urban network based on improved cell transmission model [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 132-139.
[6] Quan LIANG,Jian-cheng WENG,Wei ZHOU,Jian RONG. Stability identification of public transport commute passengers based on association rules [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1484-1491.
[7] Qiao⁃wen BAI,Zhao⁃wei QU,Yong⁃heng CHEN,Shuai XIONG,Chu⁃qing TAO. Modeling on trajectories of through vehicles with an unprotected left⁃turn phase under non⁃strict priority [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 673-679.
[8] Lei CHEN,Jiang⁃feng WANG,Yuan⁃li GU,Xue⁃dong YAN. Multi⁃source traffic data fusion algorithm based onmind evolutionary algorithm optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 705-713.
[9] Qiang TU,Lin CHENG,Fen LIN,Chao SUN. Finding shortest path considering traveler′s risk attitude [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 720-726.
[10] ZONG Fang, QI Hou-cheng, TANG Ming, LYU Jian-yu, YU Ping. Identifying daily travel pattern-trip purpose using GPG data [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1374-1379.
[11] LUAN Xin, DENG Wei, CHENG Lin, CHEN Xin-yuan. Mixed Logit model for understanding travel mode choice behavior of megalopolitan residents [J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[12] HOU Xian-yao, CHEN Xue-wu. Use of public transit information market segmentation based onattitudinal factors [J]. 吉林大学学报(工学版), 2018, 48(1): 98-104.
[13] LI Zhi-hui, WANG Kun-wei, SONG Xian-min, LIU Xin-shan, SHEN Yao, LUO Rui-qi. Roundabout travel time prediction based on characteristics of lane choosing [J]. 吉林大学学报(工学版), 2017, 47(5): 1411-1419.
[14] ZHOU Xi-yang, YANG Zhao-sheng, ZHANG Wei, BING Qi-chun, SHANG Qiang. Urban shortest path searching algorithm considering coordinate control of arterial intersections [J]. 吉林大学学报(工学版), 2016, 46(6): 1799-1806.
[15] ZONG Fang, WANG Zhan-zhong, JIA Hong-fei, JIAO Yu-ling, WU Yang. Prediction of commuter's daily activity-travel duration time with support vector regression [J]. 吉林大学学报(工学版), 2016, 46(2): 406-411.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd