## 1 Introduction  
Road traffic accidents pose significant economic and medical burdens globally, often leading to catastrophic family tragedies and accounting for approximately 1.3 million fatalities annually. However, as highlighted by the World Health Organization (WHO), injuries caused by traffic accidents can be prevented [1; 2]. With the rapid advancement of machine learning, the field of traffic accident prediction has demonstrated tremendous potential. It not only enhances the accuracy of accident prediction but also allows for real-time response to traffic conditions, reducing accident risks. Existing research has primarily focused on whether traffic accidents will occur, often neglecting the further classification of potential accidents. Ideally, all potential traffic accidents should receive significant attention, with resources mobilized to prevent their occurrence. However, due to limited resources and the need for multi-agency co-ordination, it is often challenging to address all traffic accidents promptly. Therefore, it is essential to give additional attention to the possibility of severe traffic accidents and take necessary measures to prevent their occurrence or prepare for their management in advance. Simultaneously, traffic accidents typically exhibit characteristics of imbalance, such as the far smaller number of accident samples compared to normal traffic samples [3], a significantly higher number of accidents in high-risk areas compared to low-risk areas [4], and an imbalance in the sample proportions of different accident types. Particularly, in traffic accidents, the number of severe events is much lower than that of common events. Machine learning models tend to learn this prior information regarding the proportions of the two sample classes in the training set, which results in an emphasis on the majority class during actual prediction (possibly leading to better accuracy for common accidents but poorer accuracy for severe accidents). Unfortunately, in previous work, the probability of occurrence of these minority class accidents is often overlooked, and especially the occurrence of severe accidents in traffic accidents should receive more attention. Due to the presence of a considerable number of boolean features, such as points of interest (POI), streets, wind direction, weather, and other factors in traffic accident datasets, there remains an issue of feature sparsity in traffic accident prediction. To address the aforementioned issues, this paper introduces the ReMAHA-CatBoost traffic accident prediction model, aiming to classify potential traffic accidents and to resolve the problems of imbalanced sample quantities, high-dimensional, and sparse feature-induced prediction inaccuracies in traffic accidents. This model incorporates feature selection and clustering operations into the oversampling process to improve the problem of traditional genetic algorithms generating overly divergent data. Simultaneously, it addresses the issue of Mahalanobis distance calculation during the oversampling process, caused by sparse features. Additionally, the weight matrix obtained using the relief-F algorithm not only aids in Mahalanobis distance weighted computation, increasing the impact of feature importance on sampling, but also serves for original feature selection to avoid the curse of dimensionality and overfitting. In this paper, the contributions can be summarized as follows:  
* We introduce ReMAHA-CatBoost, a traffic accident prediction model based on CatBoost [5].