## 3 Results
### Dataset  
The dataset constructed for this study provides a comprehensive resource for examining the relationship between road traffic accidents (RTAs) and meteorological conditions. It comprises three primary groups of features: accident characteristics, meteorological parameters, and socioeconomic data. Accident attributes include precise location and timing information alongside detailed site characteristics such as road geometry, surface conditions, speed limits, and intersection types. Each incident record contains administrative location data from municipality to vovodeship level. Meteorological variables encompass temperature, humidity, and precipitation measurements at both hourly and cumulative 24 h intervals for each accident location. Additionally, seasonal weather patterns for each county were captured through daily interpolated measurements, enabling analysis of meteorological trends in relation to incident frequency \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Kriging Method** & **RMSE** & **MAE** & **NRMSE** & **NMAE** \\ \hline \multirow{2}{*}{Temperature (\({}^{\circ}\)C)} & Universal & 1.500 & 1.246 & 0.656 & 0.543 \\  & Ordinary & 2.596 & 2.079 & 1.047 & 0.841 \\ \hline \multirow{2}{*}{Humidity (\%)} & Universal & 7.123 & 5.875 & 1.074 & 0.872 \\  & Ordinary & 8.028 & 6.685 & 1.202 & 0.986 \\ \hline \multirow{2}{*}{Precipitation (mm)} & Universal & 0.145 & 0.087 & 0.615 & 0.374 \\  & Ordinary & 0.145 & 0.087 & 0.685 & 0.449 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of universal and ordinary kriging methods in mountainous counties based on cross-validation results using multiple error metrics. Figure 6: Cross-validation performance comparison between universal and ordinary kriging methods in mountainous counties of southern Poland. (**a**) Normalized Root Mean Square Error (NRMSE) demonstrating universal kriging’s superior accuracy in topographically complex terrain, particularly for temperature interpolation. (**b**) Normalized Mean Absolute Error (NMAE) showing consistent performance improvements across all meteorological parameters when accounting for elevation effects. (**c**) Percentage improvement in NMAE achieved by universal kriging in mountainous regions, with substantial gains for temperature (35.4%), humidity (11.6%), and precipitation (16.7%), highlighting the critical advantage of incorporating elevation drift in complex topographic environments where terrain significantly influences meteorological patterns. across administrative regions. These values were derived through universal kriging interpolation from weather station data. Socioeconomic factors include indicators such as population density, transportation infrastructure metrics (road density, surface quality), vehicle fleet composition, and historical accident statistics. These variables provide essential baseline context for understanding regional risk patterns and normalizing accident rates across administrative units of varying sizes and development levels. Figure 9 illustrates the distributional differences between general weather conditions and those during traffic incidents. The temperature histogram reveals that traffic incidents occur disproportionately during warmer conditions, while the humidity distribution shows fewer incidents at the highest humidity levels compared to the background weather distri  
Figure 8: Average weather conditions across Polish counties. The histograms display the following: (**a**) mean temperature (°C), (**b**) mean relative humidity (%), and (**c**) precipitation frequency (%). All meteorological variables were interpolated at county centroids using universal kriging from weather station data. Temperature and humidity are presented as daily averages, while precipitation frequency represents the percentage of days with total precipitation exceeding 0.1 mm. Figure 7: Universal kriging interpolation of meteorological variables across Poland at a specific time, incorporating elevation as a drift term.