Abstract
On Polish highways, a staggering number of individuals pass away each year. The quantity is still quite large even if the value is declining year after year. The value of traffic accidents has greatly decreased due to the epidemic, but it is still quite high. In order to reduce this number, it is required to identify the roads where the majority of accidents occur and to understand the predicted number of accidents in the upcoming years. The article’s goal is to predict how many accidents will occur on Polish roads based on the kind of roads. To achieve this, monthly accident data for Poland from the Police’s statistics for the years 2007–2021 were analyzed, and a prediction for the years 2022–2024 was created. As is evident, either the number of accidents is rising or it is stabilizing. This is mostly caused by the rise in automobile traffic. Additionally, predictions indicate that given the existing circumstances, a significant rise in the number of accidents on Polish roads may be anticipated. This is especially evident in the nation’s growing number of freeways. It should be remembered that the current epidemic distorts the findings. Selected time series models were used in the investigation in Statistica.
Publisher
Highlights of Science, S.L.
Reference62 articles.
1. World Health Organization (WHO). (Ed.). (2018). The Global status on road safety 2018. https://www.who.int/publications/i/item/9789241565684 (accessed 10 February 2022).
2. Tambouratzis, T., Souliou, D., Chalikias, M., & Gregoriades, A. (2014). Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees. Journal of Artificial Intelligence and Soft Computing Research, 4(1), 31–42. https://doi.org/10.2478/jaiscr-2014-0023
3. Zhu, L., Lu, L., Zhang, W., Zhao, Y., & Song, M. (2019). Analysis of accident severity for curved roadways based on Bayesian networks. Sustainability, 11(8), 2223. https://doi.org/10.3390/su11082223
4. Arteaga, C., Paz, A., & Park, J. (2020). Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach. Safety Science, 132, 104988. https://doi.org/10.1016/j.ssci.2020.104988
5. Yang, Z., Zhang, W., & Feng, J. (2022). Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework. Safety Science, 146, 105522. https://doi.org/10.1016/j.ssci.2021.105522