# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 6 Conclusions  
The study explored factors influencing traffic accident severities in North Virginia, US, utilizing models capable of handling more than two categories in the output variables. The Multinomial Logistic Regression (MLR) and Multi-Level Multinomial Logistic Regression (MLMLR) models were utilized for analysis. The results show that the Multinomial Logistic Regression model, including Collision Type, Traffic Control, Belt Usage, Bicycle Presence,Animal Incidents, and Pedestrian Involvement, identified the key factors influencing collision severity. These variables showed statistically significant coefficients, indicating their impact on outcomes like Fatality, Major Injury, and Minor Injury. Moreover, the Multi-Level Multinomial Logistic Regression model, accommodating hierarchical data structures, provided a nuanced perspective and reaffirmed the influence of factors identified by the Multinomial Logistic Regression model. It introduced additional variables like Area Type and Roadway Description, shedding light on their roles in determining collision severity. The Multinomial Logistic Regression model highlighted significantly higher odds ratios for fatality and major injury when Pedestrians and Bicycles were involved in accidents, emphasizing the severity of such incidents. In addition, head-on collisions were substantially more likely to result in fatalities and catastrophic injuries than rear-end collisions, which were normally less severe. Collisions in places with traffic control measures had a significantly lower risk of fatality and sever injury, highlighting the protective impact of these systems. Not wearing a seatbelt significantly raised the probability of serious outcomes, underscoring the necessity of seatbelt use in lowering injury severity. In addition, collisions involving animals marginally increased the probability of death. Urban regions provided a protective impact, with a lower risk of severe outcomes compared to non-urban locations. The Multi-Level Multinomial Logistic Regression model, with its ability to account for the hierarchical nature of the data, presented an added advantage, potentially offering more robust and fine-grained distinctions in predicting collision severity outcomes. The findings contribute to a better understanding of factors influencing accident severities, providing valuable information for improving road safety strategies and mitigating the impact of collisions on individuals and the economy. To lower the probability of accidents and collision severity, road managers and designers should improve traffic management features like traffic lights and roundabouts, especially in high-risk locations. To safeguard vulnerable road users, dedicated bicycle lanes, pedestrian crosswalks, and signalized crossings can help to create safer junctions. Roads that are prone to head-on crashes should have median barriers, rumble strips, and lane separation, while urban planning principles such as lower speed limits, narrower lanes, and better lighting should be implemented to increase overall safety. Seatbelt use must be promoted by stronger rules and awareness efforts, as well as the removal of animal crash hotspots through signage, fence, and wildlife corridors. These targeted tactics can dramatically improve road safety and reduce collision risk. Future studies can consider other modeling techniques (e.g., Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks [49]) or interaction terms. Moreover, the application assessment of the proposed solutions suggested by the model can be considered in future work. In addition, future studies could distinguish between the variables that considered the causes and consequences of traffic accidents. This distinction may improve the model's capacity to appropriately estimate the elements determining accident severity. Future work can also consider assessing the severity of collisions based on traffic conflict techniques, simulation-based models [50; 51; 52; 53; 54] and risk-based design measures [55; 56; 57].