# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 1 Introduction  
Car accidents cause significant human distress and economic challenges worldwide [1]. In 2021, the National Highway Traffic Safety Administration (NHTSA) reported roughly 43,000 traffic fatalities, representing a 10.5% increase over 2020 [2]. Furthermore, fatalities resulting from speeding climbed by 5%, as did deaths from alcohol-related incidents. Predictive models for traffic accidents provide useful insights into the causes of crashes and can help reduce their occurrence. In recent years, researchers have become increasingly interested in understanding the fundamental causes of traffic accidents and building models to forecast and evaluate these incidents. Various studies have highlighted specific driver behaviours contributing to accidents, such as distracted driving (e.g., mobile device use) and impaired driving [3, 4, 5]. According to [PERSON] et al. [6], children are more likely to be fatally harmed in crashes when drivers are speeding or distracted. [PERSON] et al. [7] stated that driver behaviour contributes to traffic accidents, including road infrastructure and vehicle characteristics. Understandingthe precise factors influencing accidents is vital for policymakers and stakeholders to develop targeted strategies for prevention. A study by [PERSON] and [PERSON] [8] examined factors affecting the severity of injuries in automobile-bicycle crashes using Virginia police crash data. Intoxicated automobile drivers and bicyclists, along with factors like vehicle speed and roadway conditions, were found to impact injury severity significantly. The study emphasizes the need for measures against biking and driving under the influence and promoting road separation. The research by [PERSON] et al. [9] also highlighted the importance of considering human behaviour in road safety strategies. Their research underscores the need for effective road safety measures that account for the multifaceted role of human behaviour in traffic collisions. Research by [PERSON] et al. [10], focusing on crash severity at freeway entrance and exit ramp interchanges, showed common crash types in Northern Virginia and emphasized factors like speed, weather conditions, and alcohol as contributors to crash severity. Moreover, [PERSON] and [PERSON] [11] analyzed road casualties in England, focusing on high-risk areas characterized by poverty, low car ownership, and elevated speed limits. Their findings recommend directing road safety efforts towards these locales to reduce road mishaps. [PERSON] et al. [12] also investigated factors affecting fatalities and severe injuries in bicycle accidents in Victoria, Australia. Their study highlighted predictors such as the cyclist's age and gender, time of day, and infrastructure quality, emphasizing the potential for infrastructure improvements and educational initiatives. This study delves into the intricate factors influencing collision severity, focusing prominently on human behaviour. It investigates a range of variables, including pedestrian conduct, cyclist interactions, vehicle characteristics, weather conditions, and road attributes. Assessing the severity of traffic accidents is critical for understanding the factors that lead to more serious outcomes and guiding efforts to reduce fatalities and injuries. The severity of accidents, on the other hand, varies greatly between geographical areas--such as urban vs. rural settings, districts, and times of day--influencing the sorts of crashes and their results. Predictive models that assess these variable conditions can provide critical insights into accident causes and assist in tailoring safety measures to specific areas, thereby enhancing road safety and lowering accident-related injuries. By employing advanced predictive models like Multinomial Logistic Regression and Multi-Linear Multinomial Logistic Regression, the study aims to deepen our understanding of collision severity dynamics beyond traditional methodologies.