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Safety-Critical Event Identification on Mountain Roads for Traffic Safety and Environmental Protection Using Support Vector Machine with Information Entropy

Author

Listed:
  • Zihao Wen

    (Guangdong Provincial Key Laboratory of Intelligent Transport System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China)

  • Hui Zhang

    (Guangdong Provincial Key Laboratory of Intelligent Transport System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China)

  • Ronghui Zhang

    (Guangdong Provincial Key Laboratory of Intelligent Transport System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

Traffic accidents, which cause loss of life and pollution, are a social concern. The complex traffic environment on mountain roads increases the harm caused by traffic accidents. This study aimed to identify safety-critical events related to accidents on mountain roads to understand the causes of the accidents, improve traffic safety, and protect the environment. In this study, a naturalistic-driving data collection system, consisting of approximately 8000 km of naturalistic-driving data from 20 drivers driving on mountain roads, was developed. Using these data, a comparative analysis of the identification performance of the support vector machine (SVM), backpropagation neural network (BPNN), and convolutional neural network (CNN) methods was conducted. The SVM was found to yield optimal performance. To improve the identification performance, the yaw rate and information entropy of the data were added as input variables. The improved SVM method yielded an identification accuracy of 90.64%, which was approximately 15% higher than that yielded by the traditional SVM. Moreover, the false positive and false negative rates of the improved SVM were reduced by approximately 10% and 20%, respectively, compared with the traditional SVM. The results demonstrated that the improved SVM method can identify safety-critical events on mountain roads accurately and efficiently.

Suggested Citation

  • Zihao Wen & Hui Zhang & Ronghui Zhang, 2021. "Safety-Critical Event Identification on Mountain Roads for Traffic Safety and Environmental Protection Using Support Vector Machine with Information Entropy," Sustainability, MDPI, vol. 13(8), pages 1-15, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4426-:d:536952
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    Citations

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    Cited by:

    1. Fu Wang & Jing Wang & Xianfeng Zhang & Dengjun Gu & Yang Yang & Hongbin Zhu, 2022. "Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    2. Bogyeong Lee & Sungjoo Hwang & Hyunsoo Kim, 2021. "The Feasibility of Information-Entropy-Based Behavioral Analysis for Detecting Environmental Barriers," IJERPH, MDPI, vol. 18(21), pages 1-14, November.
    3. Sajjad Ahadzadeh & Mohammad Reza Malek, 2021. "Earthquake Damage Assessment Based on User Generated Data in Social Networks," Sustainability, MDPI, vol. 13(9), pages 1-19, April.

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