## 1 Introduction  
Road traffic accidents remain a pressing global issue, claiming approximately 1.19 million lives annually, according to the World Health Organization (WHO) [1]. In the United States, for example, the year 2020 saw around 40,000 fatalities due to traffic collisions, with an additional 2.1 million people requiring emergency medical care as a result of such incidents [2]. Beyond the tragic human toll, the economic burden is immense, with annual costs in the U.S. alone reaching an estimated USD 430 billion--a figure that accounts for healthcare expenses, productivity losses, and the diminished quality of life [3]. On a global scale, the WHO reports that road crashes cost countries, on average, roughly 3% of their gross domestic product (GDP) [1]. The field of traffic safety research has evolved significantly over the decades, driven by the dual objectives of understanding the complex nature of road traffic crashes and developing effective interventions to reduce their occurrence and severity. At the heart of this research lies crash prediction modeling, a methodology used for identifying the underlying factors contributing to road crashes' occurrence and the resulting traffic injuries and fatalities [4]. These factors include, but are not limited to, traffic volume, road geometry, environmental conditions, vehicle characteristics, and driver behavior. Crash prediction modeling is a statistical approach that is used to analyze how various predictors affect crash severity and frequency. The primary focus is on identifying relationships that help predict the occurrence of crashes and the likelihood of different injury types. Crash severity modeling aims at identifying influential factors in the severity of injuries, where outcomes are confined to categories such as those in the KABCO scale. Crash frequency modeling, on the other hand, quantitatively assesses the relationship between crash counts and their influencing factors. Historically, crash frequency analysis has relied heavily on count models, with the Poisson and negative binomial models being particularly prominent due to their suitability for handling count data [4; 5]. These models have been instrumental in establishing relationships between crash occurrences and a wide array of risk factors, thereby facilitating the prediction of crashes and the assessment of their severity across different roadway environments. These models serve as the foundation for the development of Safety Performance Functions (SPFs), which are extensively used in the _Highway Safety Manual_ to estimate crash frequency and conduct road safety analysis [6]. Despite these advancements, researchers and practitioners in the field have encountered significant challenges when dealing with datasets characterized by a high prevalence of zero-crash instances and excessive dispersion. Such datasets, often resulting from the low frequency of crashes at individual sites or within specific time periods, present a skewed distribution that traditional count models are not able to handle efficiently. This issue is due to the fact that crash frequency data are mostly characterized by overdispersion, with the variance of crash counts typically exceeding the mean, leading to model misspecification and biased risk factor estimates [7; 8]. In response to these limitations, this study introduces an interpretable machine learning-based hurdle model framework tailored for analyzing zero-inflated crash frequency data. The research adopts a two-stage modeling process that utilizes advanced machine learning algorithms (specifically, CatBoost). The first stage employs a binary classification to identify road segments with potential crash occurrences, addressing data imbalance through a customized loss function. The second stage focuses on predicting the frequency of crashes given their occurrence, again using a customized loss function to handle count data.