## 3 Results
### Logistic Regression Analysis Results  
Binomial logistic regression shows the strength of relationships between accident-related variables and severe accidents, identifying high-risk variables with significant odds ratios. The logistic regression analysis in Table 3 reveals several key variables that significantly increase the likelihood of severe RTAs. One of the most influential factors is the non-use of a safety belt, which has the highest odds ratio (\(\exp(\beta)=5.015\)) among the variables analysed, indicating that drivers not wearing a seatbelt are over five times more likely to be involved in a severe accident. Other critical factors include young drivers, particularly those aged 16 to 18, who have an odds ratio of 2.317, and those aged 19 to 24, with an odds ratio of 2.101, reflecting a significantly higher risk for these age groups. Environmental and situational factors also play a substantial role. Driving during early morning hours (12 a.m. to 6 a.m.) increases the likelihood of severe accidents by 35.9% (\(\exp(\beta)=1.359\)), likely due to factors such as fatigue and reduced visibility. Road conditions, such as driving on a wet road or under wintry conditions, also contribute to higher accident severity, with odds ratios of 1.261 and 1.462, respectively. The presence of specific road features like curves, intersections, and tunnels significantly increases the risk, with tunnels showing an odds ratio of 1.674 and curves 1.198, indicating these features are critical risk factors. Vehicle-related factors also influence the severity of accidents. Vehicles with engine power between 24 and 90 kW show a 19,2 % higher likelihood of severe accidents, while certain actions like sudden braking or hitting an obstacle on the road increase the risk significantly, with odds ratios of 2,0 and 3.394, respectively. Interestingly, hitting a guardrail is associated with a lower likelihood of severe accidents, with an odds ratio of 0.731, suggesting that this might serve as a mitigating factor under certain conditions. The analysis also highlights the significant impact of alcohol, which nearly doubles the likelihood of severe accidents (\(\exp(\beta)=1.916\)), underscoring the critical danger posed by impaired driving. Additionally, vehicle-related variables like the colour green and the absence of airbag deployment are associated with higher risks, with odds ratios of 1.317 and 2.233, respectively. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variable** & \begin{tabular}{c} **Regression** \\ **Coefficient**\(\beta\) \\ \end{tabular} & \begin{tabular}{c} **Standard Error** \\ **SEM** \\ \end{tabular} & \
\begin{tabular}{c} **p** \\ \end{tabular} &
\begin{tabular}{c} **exp(\(\beta\))** \\ \end{tabular} \\ \hline No safety belt applied & 1.612 & 0.062 & 0.000 & 5.015 \\ \hline Gallery & 1.522 & 0.589 & 0.010 & 4.583 \\ \hline Vehicle fire & 1.394 & 0.541 & 0.010 & 4.029 \\ \hline Hitting an obstacle on the road & 1.222 & 0.426 & 0.004 & 3.394 \\ \hline Age class 16 to 18 & 0.840 & 0.104 & 0.000 & 2.317 \\ \hline Airbag not deployed & 0.803 & 0.046 & 0.000 & 2.233 \\ \hline Bridge & 0.773 & 0.197 & 0.000 & 2.166 \\ \hline Age class 19 to 24 & 0.743 & 0.057 & 0.000 & 2.101 \\ \hline Sudden braking & 0.693 & 0.324 & 0.032 & 2.000 \\ \hline Alcohol & 0.650 & 0.062 & 0.000 & 1.916 \\ \hline Hit and run & 0.552 & 0.161 & 0.001 & 1.737 \\ \hline Tunnel & 0.515 & 0.258 & 0.046 & 1.674 \\ \hline One-way & 0.507 & 0.219 & 0.020 & 1.660 \\ \hline Age class 25 to 34 & 0.492 & 0.057 & 0.000 & 1.635 \\ \hline Male driver & 0.491 & 0.045 & 0.000 & 1.634 \\ \hline Intersection & 0.450 & 0.148 & 0.002 & 1.569 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Logistic regression analysis results. When performing multiple logistic regression, some variables may be excluded from the final model, resulting in no regression coefficient being assigned to them.