Management and forecasting of road accidents in Poland and Montenegro using neural networks

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Abstract
Despite a general decline in recent years, road traffic accidents remain a significant public safety concern in both Poland and Montenegro. Although accident rates were affected by the COVID-19 pandemic, the persistent frequency of such incidents underscores the urgent need for further preventive measures to enhance road safety. The aim of this study is to forecast the number of road traffic accidents in Poland and Montenegro for the period 2024-2030. To achieve this, historical data on annual accident counts were obtained from Monstat (Montenegro) and the Polish Police. These datasets were then analyzed using selected neural network models to generate projections for the specified timeframe. The results suggest a potential stabilization in the number of traffic accidents in the near future. This forecast is influenced by several factors, including the steady increase in car ownership and ongoing investments in road infrastructure, such as the construction of new motorways and local roads. It should be noted, however, that the inherent uncertainty in data sampling – used for training, testing, and validating the models – places natural limitations on the precision of the forecasts.

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