| Autor: |
Shala, Ferat, Demolli, Halil, Sejdiu, Liridon |
| Předmět: |
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| Zdroj: |
International Review of Civil Engineering; Mar2024, Vol. 15 Issue 2, p201-208, 8p |
| Abstrakt: |
The number of accidents is increasing due to the rising movement of vehicles. However, various factors have a direct impact, including meteorological conditions. This study has aimed to investigate the relationship between accidents and weather conditions by using machine learning algorithms. Certain scenarios have been conducted to reveal the performances of these algorithms and the impact of those factors on accidents. The results have demonstrated a correlation between accidents and weather conditions, with a stronger association observed during the winter season. Notably, the presence of fog has emerged as the weather factor with the greatest influence. These findings provide valuable insights for understanding the impact of weather conditions on accident occurrences and emphasize the importance of considering weather-related factors in accident prevention and road safety measures. The results indicate a strong relationship between accidents and atmospheric conditions, especially with the Random Trees algorithm by achieving a correlation value of 0.988. Through this algorithm, it has been shown that atmospheric conditions have a better correlation with accident occurrences during the winter season. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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