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Using Neural Networks to Forecast the Amount of Traffic Accidents in Poland and Lithuania

Author

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  • Piotr Gorzelańczyk

    (Department of Transport, Stanislaw Staszic State University of Applied Sciences in Pila, Podchorazych 10 Street, 64-920 Pila, Poland)

  • Edgar Sokolovskij

    (Department of Automobile Engineering, Faculty of Transport Engineering, Vilnius Gediminas Technical University, Plytinės g. 25, 10105 Vilnius, Lithuania)

Abstract

Globally, and specifically in Poland and Lithuania, the incidence of road accidents has been on a decline over the years. The overall figures remain significantly high. Thus, it is imperative to take substantial measures to further decrease these statistics. The objective of this article is to estimate the future frequency of traffic accidents in both countries. To achieve this, a comprehensive yearly analysis of traffic incidents in Poland and Lithuania was performed. Using police records, forecasts for the years from 2024 to 2030 were established. Various neural network models were employed to predict the number of accidents. The results suggest that there remains potential for stabilization in traffic accident rates. It is undeniable that the increasing volume of vehicles on the roads, along with the development of new highways and expressways, plays a crucial role in this scenario. The result obtained depends on the model parameters (testing, validation, and training phases). Sustainable development requires comprehensive solutions, which also include improving road safety. Our research contributes to this goal by creating a tool that provides insight into the number of road accidents in analyzed countries.

Suggested Citation

  • Piotr Gorzelańczyk & Edgar Sokolovskij, 2025. "Using Neural Networks to Forecast the Amount of Traffic Accidents in Poland and Lithuania," Sustainability, MDPI, vol. 17(5), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1846-:d:1596883
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    References listed on IDEAS

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    1. Helgason, Agnar Freyr, 2016. "Fractional Integration Methods and Short Time Series: Evidence from a Simulation Study," Political Analysis, Cambridge University Press, vol. 24(1), pages 59-68, January.
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