# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 2 Literature Review  
Several previous studies have been devoted to understanding factors that lead to varying degrees of severity in traffic collisions. Statistical methodologies have been the key for traffic collision severity prediction. [PERSON] et al. studied the factors influencing the frequency of pedestrian-motor vehicle incidents in Scotland, especially at crossings and junctions. The study utilized the correlated random parameter ordered probit models to account for unobserved heterogeneity. The results highlighted a number of significant components, including road, location, weather, vehicle, driver behavior, and timing considerations. Particularly in weather, dangers, and time factors, there were observed differences between signalized and unsignalized intersections and crossings controlled either physically or by humans [13]. [PERSON] et al. analyzed accident data in Los Angeles to determine the factors that influence pedestrian injury severity. They investigated critical factors and their consistency throughout time using a logit model with random parameters. The study found that key variables changed throughout a seven-year period, highlighting the importance of dynamic examination of crash data [14]. [PERSON] et al. analyzed pedestrian-motor vehicle crash data in North Carolina to identify characteristics affecting pedestrian injury severity. Using random parameter logit models, the study found that ambulance rescue and curved roadways had a consistent impact on pedestrian injury severity [15]. [PERSON] et al. presented a two-level random intercept model using Bayesian probability inference for forecasting pedestrian crash severity. The model's utility was demonstrated using pedestrian crash data from Colorado, and the results revealed better prediction accuracy compared to previous methods [16]. [PERSON] et al. used a random parameter multinomial logit model to identify accident characteristics that were strongly associated with kid pedestrian crashes. The study found that children are more likely to be fatally harmed in crashes when drivers are speeding or distracted [6]. Previous studies have established that the lives of vulnerable road users such as cyclists and pedestrians are particularly at higher risk when encountering vehicle operators. For instance, a study conducted by [PERSON] and [PERSON] [17] found that, while pedestrians and cyclists are involved in traffic accidents, they tend to suffer from severe injuries and higher fatality rates than the occupants of motor vehicles. Similarly, [PERSON] et al. [18] identified that road locations with more pedestrians and cyclists riding by states are more dangerous in terms of resulting collisions; therefore, safety measures should be directed. [PERSON] et al. [19] investigated the link between road accidents and weather conditions in the Czech Republic. The dataset comprised annual information on general traffic accidents, Property Damage Occurrences, injuries (both major and minor), and fatalities. Seven weather categories were utilized to categorize circumstances at the time of the accident. Among them, \"rain\", \"onset of rain and light rain\", \"snowfall\", and \"glaze ice and rime\" were recognized as the most critical, causing the highest incidence of accidents, injuries, and deaths. Additionally, through a study conducted on the impacts of antecedent climate conditions on collision severity outcomes noted by [PERSON] et al. [20] collision severity was associated with numerous risks, including extreme weather events. The geometry of roads and their other features are vital in determining the probability of traffic accidents. According to the study by [PERSON] et al. [21], road characteristics such as intersections, pedestrian crossroads, and the road's surface also determine the extent of collision.