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Fuzzy-Based Road Accident Risk Assessment

Author

Listed:
  • Péter Mogyorósi

    (Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, H-1034 Budapest, Hungary)

  • Sándor Szénási

    (John von Neumann Faculty of Informatics, Óbuda University, H-1034 Budapest, Hungary
    Current address: Faculty of Economics and Informatics, J. Selye University, P.O. Box 54, Komarno, Slovakia.)

  • Edit Laufer

    (Bánki Donát Faculty of Mechanical and Safety Engineering, Óbuda University, H-1034 Budapest, Hungary)

Abstract

It is necessary to extensively investigate the causes of road accidents with the utmost precision to harness future technological advancements, such as autonomous driving and intelligent accident prevention systems. Nevertheless, since most accidents are attributed to simple human errors, unraveling the complex root-cause factors poses a considerable challenge. This is where fuzzy logic can offer a potential solution: it is essential to understand even seemingly straightforward errors, such as speeding, to identify external factors that could play a pivotal role in future accident prevention. A more in-depth examination and comprehension of elements like road curvature, slope, and their correlation with accidents are necessary. Additionally, it is crucial to explore how the frequency of accidents on specific road segments varies under diverse weather conditions. This article analyzes which curves can be considered more dangerous and the factors that render them risky. The fuzzy model presented in this article is primarily capable of estimating the risk of a given road segment based on its curvature characteristics. The model results presented in the article indicate that sections of the road can become more risky due to multiple curves and curves with a radius of less than 80 m. The model assesses risk based on the physical characteristics of road segments, primarily the curvature radius, while, typically, other road risk assessment models rely on traffic volume and accident counts.

Suggested Citation

  • Péter Mogyorósi & Sándor Szénási & Edit Laufer, 2024. "Fuzzy-Based Road Accident Risk Assessment," Mathematics, MDPI, vol. 12(8), pages 1-18, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1144-:d:1373474
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    References listed on IDEAS

    as
    1. Yaolong Liu & Xiaoli Huang & Jin Duan & Huaming Zhang, 2017. "The assessment of traffic accident risk based on grey relational analysis and fuzzy comprehensive evaluation method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1409-1422, September.
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