# Analyzing Important Elements for Improving the Safety of Motorways
###### Abstract  
This study aims to identify the factors that influence the occurrence of traffic accidents to improve motorway traffic safety. Various data, including the frequency of traffic accidents, traffic volume, geometric structure, and congestion level, were collected from individual sections of motorways in South Korea. Using the collected data, a traffic accident frequency prediction model was developed by applying an explainable artificial intelligence (AI)-based approach. The developed deep neural network model was combined with Shapley Additive Explanations to identify the variables that significantly affect the frequency of traffic accidents. The analysis identified five significant factors: segment length, total traffic volume, the proportion of truck traffic, the number of dangerous driving behaviors, and the duration of congestion. The results demonstrate the potential of using explainable AI in predicting traffic accident frequency. By identifying the factors that influence traffic accidents using this model, we can pinpoint areas for improvement, which may ultimately help reduce highway traffic accidents. motorway; safety; deep neural network; explainable AI (XAI); Shapley Additive Explanation (SHAP) +
Footnote †: journal: Applied sciences  
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snowfall [6; 7]. Initially, linear regression models were commonly used to represent the relationship between key factors and the frequency of traffic accidents [8]. These models assume that the dependent variable, the frequency of traffic accidents, follows a normal distribution [9]. However, this assumption does not adequately reflect the characteristics of traffic accidents, which occur randomly and rarely. Therefore, to overcome the limitations of linear regression models, Poisson regression models, negative binomial regression models, multinomial probability models, and Bayesian models have been used [10]. Statistical models for more accurately estimating the frequency of traffic accidents have continuously evolved over a long period. Despite this, the factors influencing traffic accidents are highly diverse and exhibit complex interrelationships, making it extremely challenging to identify and model these relationships accurately. Consequently, models utilizing artificial intelligence (AI), which have superior predictive performance compared to traditional statistical models, have recently emerged. AI-based approaches offer several advantages: first, AI can efficiently process large volumes of data; second, AI can build models even if the interrelationships between data are not clear; and finally, AI models can autonomously learn important features, resulting in relatively high predictive accuracy. However, there are limitations in understanding the basis on which AI estimates the frequency of traffic accidents. Fully explaining AI's reasoning is extremely challenging, a phenomenon known in the field of AI as the \"black box\" problem [11]. One approach to addressing this issue is the use of explainable AI (XAI) [12]. The primary question addressed in this study is: \"What are the key factors influencing traffic accident occurrences, and are these factors consistently identified across traditional statistical models and AI-based models?\" To achieve this, various data--including the frequency of traffic accidents, traffic volume, geometric structure, and congestion level--were collected from individual sections of motorways in South Korea. Following the research objective, traditional statistical models and AI-based models were employed to identify key variables significantly influencing traffic accidents. Furthermore, the AI-based model was integrated with XAI to explain the variables that have a meaningful impact on traffic accident frequency. This study bridges an important gap in the field by ensuring the consistency and reliability of key traffic accident factors through the use of both traditional statistical methods and AI-based methodologies.