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Relationship between Highway Geometric Characteristics and Accident Risk: A Multilayer Perceptron Model (MLP) Approach

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  • Jie Yan

    (National Engineering Laboratory of Land Traffic Meteorological Disaster Prevention and Control Technology, Yunnan Transportation Planning and Design Institute Co., Ltd., Kunming 6502001, China
    Yunnan Key Laboratory of Digital Communications, Kunming 650103, China
    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    These authors contributed equally to this work.)

  • Sheng Zeng

    (National Engineering Laboratory of Land Traffic Meteorological Disaster Prevention and Control Technology, Yunnan Transportation Planning and Design Institute Co., Ltd., Kunming 6502001, China
    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
    These authors contributed equally to this work.)

  • Bijiang Tian

    (National Engineering Laboratory of Land Traffic Meteorological Disaster Prevention and Control Technology, Yunnan Transportation Planning and Design Institute Co., Ltd., Kunming 6502001, China
    Yunnan Key Laboratory of Digital Communications, Kunming 650103, China)

  • Yuanwen Cao

    (School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Wenchen Yang

    (National Engineering Laboratory of Land Traffic Meteorological Disaster Prevention and Control Technology, Yunnan Transportation Planning and Design Institute Co., Ltd., Kunming 6502001, China
    Yunnan Key Laboratory of Digital Communications, Kunming 650103, China)

  • Feng Zhu

    (School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore)

Abstract

The traffic safety of mountain highway has always been one of the taking point. This study aims to collect road design data in large-scale research and analyzes the accident risk of highway geometric alignment. Accordingly, a method based on satellite maps and clustering algorithms is proposed to calculate the geometric alignment of the highway plane and its longitudinal section. The reliability of the method was verified on Nanfu highway in Chongqing, China. The planar and longitudinal sectional geometries of the four highways in Chongqing were obtained by the above method, and the corresponding 36,439 traffic accidents which occurred from 2010 to 2016 were used as the research objects. The accident risk of the highway geometry was analyzed based on the SHAP and MLP theories. The results show that the fitting and prediction abilities of the MLP model are better than those of the negative binomial model, and its correlation coefficient is improved by 33.2%. In addition, compared with the negative binomial model, the MLP model can estimate more accurately and flexibly the complex nonlinear relationship between the independent and the dependent variables.

Suggested Citation

  • Jie Yan & Sheng Zeng & Bijiang Tian & Yuanwen Cao & Wenchen Yang & Feng Zhu, 2023. "Relationship between Highway Geometric Characteristics and Accident Risk: A Multilayer Perceptron Model (MLP) Approach," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1893-:d:1040500
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    References listed on IDEAS

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    1. Syyed Adnan Raheel Shah & Naveed Ahmad & Yongjun Shen & Ali Pirdavani & Muhammad Aamir Basheer & Tom Brijs, 2018. "Road Safety Risk Assessment: An Analysis of Transport Policy and Management for Low-, Middle-, and High-Income Asian Countries," Sustainability, MDPI, vol. 10(2), pages 1-30, February.
    2. Yu-Long Pei & Yong-Ming He & Bin Ran & Jia Kang & Yu-Ting Song, 2020. "Horizontal Alignment Security Design Theory and Application of Superhighways," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    4. Rainer Winkelmann, 2000. "Seemingly Unrelated Negative Binomial Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 553-560, September.
    5. Chenzhu Wang & Fei Chen & Jianchuan Cheng & Wu Bo & Ping Zhang & Mingyu Hou & Feng Xiao, 2020. "Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-13, November.
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