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Predicting black ice-related accidents with probabilistic modeling using GIS-based Monte Carlo simulation

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  • Seok Bum Hong
  • Hong Sik Yun

Abstract

Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents.

Suggested Citation

  • Seok Bum Hong & Hong Sik Yun, 2024. "Predicting black ice-related accidents with probabilistic modeling using GIS-based Monte Carlo simulation," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0303605
    DOI: 10.1371/journal.pone.0303605
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    References listed on IDEAS

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    1. Rillagoda G N Yasanthi & Babak Mehran & Wael K M Alhajyaseen, 2021. "Modelling speed behaviour in rural highways: Safety analysis of driving under adverse road-weather conditions," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-31, August.
    2. Santiago Sánchez González & Felipe Bedoya-Maya & Agustina Calatayud, 2021. "Understanding the Effect of Traffic Congestion on Accidents Using Big Data," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    3. repec:plo:pone00:0153742 is not listed on IDEAS
    4. Imran Ashraf & Soojung Hur & Muhammad Shafiq & Yongwan Park, 2019. "Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-29, October.
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