## 4 Discussion  
The findings from this study underscore the complex and multivariate nature of RTAs, particularly single-vehicle, single-occupant accidents outside built-up areas in Austria. By applying statistical methods such as binomial logistic regression and the PATTERMAX method, we have identified significant BPs that consistently correlate with severe casualties. These BPs provide critical insights into how specific combinations of driver-related, vehicle-related, roadway-related, and situational factors contribute to the severity of accidents. The multivariate approach used here echoes the work of [6], who found that considering multiple interacting factors is crucial for understanding RTA risk. One of the key observations from the logistic regression analysis is the substantial impact of not wearing a seatbelt, which emerged as the most influential variable, increasing the likelihood of severe accidents by over five times. This finding aligns with existing literature, such as [33], which consistently highlights the protective benefits of seatbelt usage in preventing severe injuries and fatalities. Seatbelt non-use remains one of the most critical behavioural risk factors in RTAs, as demonstrated in several international studies [34; 35], which also emphasise the need for stricter enforcement of seatbelt laws to mitigate injury severity. Similarly, the significant influence of young drivers, particularly those aged 16 to 24, on accident severity is consistent with previous research that points to younger drivers' higher propensity for risky behaviours, such as speeding and distracted driving [36]. Studies by [37] also note the higher incidence of severe accidents among this demographic due to inexperience and impulsive driving behaviours. The PATTERMAX method further refines our understanding by identifying specific combinations of variables that, when occurring together, significantly increase the likelihood of severe outcomes. For instance, BPs involving high-speed limits, rural roadways, and male drivers frequently result in severe accidents, especially when compounded by factors such as driver fatigue or adverse weather conditions. This mirrors findings from studies like [38] that show how rural roads, higher speeds, and male drivers increase  
accident risk, particularly in environments with poor weather or lighting conditions. The importance of considering such multivariate patterns in road safety interventions is also emphasised in work by [39], who recommend tailored safety measures for rural roads with high-speed limits. Moreover, the BP impact analysis introduces a novel way of quantifying the combined effect of these variables, offering a clear prioritisation of the most dangerous combinations. This is particularly useful for designing targeted interventions that can address the most critical risks. For instance, the combination of a 100 km/h speed limit, a country road, left drift, and a male driver not wearing a seatbelt was identified as having the highest BIS, making it a prime target for road safety campaigns and enforcement measures. This is consistent with recommendations from [40], who suggests that high-risk locations and behaviours must be prioritised for intervention based on empirical accident data. Similarly, [41] highlights the effectiveness of focusing on specific behavioural interventions, such as enforcing speed limits and seatbelt use, especially in rural areas, to reduce severe accidents. In conclusion, this study highlights the importance of multivariate approaches in road safety research and supports the growing body of literature that calls for a comprehensive, data-driven approach to accident prevention. Future research could benefit from incorporating more advanced machine learning techniques, as these methods are particularly well-suited to detecting complex patterns in large datasets, as demonstrated by [42].