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Black-Spot Analysis in Hungary Based on Kernel Density Estimation

Author

Listed:
  • Dávid Baranyai

    (Department of Transport Technology and Economics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
    E-Educatio Információtechnológia Zrt., 1111 Budapest, Hungary)

  • Tibor Sipos

    (Department of Transport Technology and Economics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
    KTI—Institute for Transport Sciences, 1119 Budapest, Hungary)

Abstract

Between 2010 and 2020 in the European Union, 30% of road accidents resulted in the death of a pedestrian or a cyclist. Accidents of unprotected pedestrians and cyclists are the reason why it is essential to introduce road safety measures. In our paper, we identify and rank black spots using an innovative reactive approach based on statistics. We elaborate on the mathematical methodological considerations through the processing of real-life empirical data in a Matlab environment. The applied black-spot analysis is based on a Kernel density estimate method, and the importance of the kernel functions and bandwidth are elaborated. Besides, special attention is devoted to the distorting effect of annual average daily traffic. The result of our research is a new methodology by which the real locations of the examined black spots can be determined. Furthermore, the boundaries of the critical sections and the extent of the formation of black spots can be determined by the introduced mathematical methods. With our innovative model, the black spots can be ranked, and the locations having the highest potential for improvement can be identified. Accordingly, optimal measures can be determined considering social-economic and sustainability aspects.

Suggested Citation

  • Dávid Baranyai & Tibor Sipos, 2022. "Black-Spot Analysis in Hungary Based on Kernel Density Estimation," Sustainability, MDPI, vol. 14(14), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8335-:d:857851
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    References listed on IDEAS

    as
    1. Yanjun Shi & Lingling Lv & Hao Yu & Liangjie Yu & Zihui Zhang, 2020. "A Center-Rule-Based Neighborhood Search Algorithm for Roadside Units Deployment in Emergency Scenarios," Mathematics, MDPI, vol. 8(10), pages 1-27, October.
    2. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
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