## 6 Conclusions  
In addressing the critical challenges faced by traditional statistical models in traffic safety analysis, particularly their limitations with zero-inflated and overdispersed data, this study proposes a novel data-driven approach through the development of a machine learning-based hurdle model. This initiative was grounded in the need to enhance the predictive accuracy and interpretability of crash frequency analyses, critical components for the formulation of effective road safety policies and interventions. The motivation for this research originated from a comprehensive review of existing methodologies in traffic safety analysis, which highlighted significant gaps in the ability of traditional models to accurately capture the complexities of crash data. Recognizing the potential of machine learning to address these gaps, we embarked on a journey to develop, validate, and test a new model capable of offering deeper insights into crash risk factors and their interactions. The machine learning-based hurdle model developed in this study represents an important methodological advancement in the field of traffic safety analysis. It is characterized by its dual-stage process, wherein the first stage addresses the probability of zero versus non-zero outcomes (zero inflation) and the second stage models the frequency of crashes given that they occur. This approach allows for a more nuanced analysis of crash data, overcoming the limitations inherent in traditional Poisson and negative binomial models. Despite the complexity often associated with machine learning models (black-box models), efforts were made to enhance the interpretability of the hurdle model, integrating SHAP values to elucidate the influence of various predictors on crash frequency outcomes. The proposed methodology encompassed not only the model's theoretical formulation but also its empirical application to real-world crash data from Korean roads. These steps were critical in demonstrating the model's effectiveness and robustness, affirming its potential utility in traffic safety research and policymaking. The practical implications of the findings are significant. The enhanced predictive accuracy and interpretability of the proposed model could lead to better-informed decision-making in traffic safety. Crash frequency modeling is useful for network screening, Safety Performance Functions (SPFs), Crash Modification Factors (CMFs), and other applications to identify high-risk locations and evaluate the effectiveness of road safety interventions. Policymakers and traffic safety professionals can use these insights to design more effective interventions, potentially reducing the frequency and severity of road traffic accidents. The framework's adaptability also means that it can theoretically be applied in various geographical contexts, making it a valuable tool for global traffic safety improvements, since it is a data-driven approach. Additionally, the ability to interpret model outputs through SHAP values allows for more transparent and justifiable policy decisions, which can improve public trust and the effectiveness of safety measures. Despite its strengths, this study has several limitations. The model's performance is tied to data quality, which may limit its generalizability beyond South Korean roads. Short road segments in the dataset may have contributed to a high incidence of zero crashes, suggesting that future research could explore longer segments to improve accuracy. Additionally, the validation process lacked extensive training/testing splits, impacting its robustness. Future work should incorporate rigorous validation techniques and consider temporal dynamics, such as evolving traffic patterns and regulations, to enhance the model's long-term relevance. Data collection biases, like underreporting, also pose challenges; thus, improving data practices is essential for fairer predictions. Further in terpretability methods, such as ICE and ALE plots, could deepen insights into variables' impacts on crash risks.