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Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

Author

Listed:
  • Tianpei Tang

    (School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
    Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Senlai Zhu

    (School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China)

  • Yuntao Guo

    (Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Xizhao Zhou

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Yang Cao

    (School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China)

Abstract

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

Suggested Citation

  • Tianpei Tang & Senlai Zhu & Yuntao Guo & Xizhao Zhou & Yang Cao, 2019. "Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1166-:d:218873
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    References listed on IDEAS

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    1. Huiying Wen & Xuan Zhang & Qiang Zeng & Jaeyoung Lee & Quan Yuan, 2019. "Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data," IJERPH, MDPI, vol. 16(2), pages 1-12, January.
    2. Isaac Dialsingh, 2014. "Risk assessment and decision analysis with Bayesian networks," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 910-910, April.
    3. Marquez, David & Neil, Martin & Fenton, Norman, 2010. "Improved reliability modeling using Bayesian networks and dynamic discretization," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 412-425.
    4. Longyu Shi & Nigar Huseynova & Bin Yang & Chunming Li & Lijie Gao, 2018. "A Cask Evaluation Model to Assess Safety in Chinese Rural Roads," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
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    Cited by:

    1. Qi Yuan & Hongqinq Zhu & Xiaolei Zhang & Baozhen Zhang & Xingkai Zhang, 2022. "An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires," IJERPH, MDPI, vol. 19(24), pages 1-26, December.
    2. Xiao Zhang & Xiaofeng Hu & Yiping Bai & Jiansong Wu, 2020. "Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    3. Rachel Aldred & Susana García-Herrero & Esther Anaya & Sixto Herrera & Miguel Ángel Mariscal, 2019. "Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment," IJERPH, MDPI, vol. 17(1), pages 1-16, December.

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