## 2 Related Work  
In the field of traffic accident research, accurate accident prediction contributes to the identification of traffic hazards, optimization of traffic system resource allocation, and timely provision of medical assistance. Currently, there have been many excellent research achievements in the domain of traffic accident prediction [6; 7; 8], and they have played a crucial role in revealing the mechanisms behind traffic accident occurrences [9]. For example, [PERSON] et al. [10] used the least-squares method to analyze the relationship between environmental factors and accidents, identifying correlations between rainfall, temperature, wind speed, and accidents. [PERSON] et al. [11] conducted association rule mining and classification studies, revealing that physiological factors such as alcohol consumption are more likely to lead to fatalities in accidents, while the impact of natural environmental factors like weather on the fatality rate is relatively smaller. [PERSON] et al. [12] employed the a priori algorithm based on association rules to explore influential factors in traffic accidents and investigate strong association rules among various causal factors. Through our investigation, we found that existing research often focuses on the causes and underlying factors of accident occurrences, using this information to predict whether traffic accidents will happen. Furthermore, most studies conduct single-dimensional analyses, only allowing for shallow data analysis and making it difficult to express the spatiotemporal correlations in traffic accidents [13]. Moreover, these studies do not conduct further classification of traffic accidents. Offering only the set of conditions that may trigger a traffic accident, such coarse-grained information also has limited contributions to the traffic system. Simultaneously, due to the phenomenon of imbalanced samples in historical traffic accident data, especially where the number of severe traffic accidents is significantly lower than that of common accidents, traditional prediction models demonstrate poor performance. Currently, there are also many outstanding works addressing the common problem of sample imbalance, which has led to improved accuracy for minority class samples in prediction models. Broadly, their approaches mainly fall into two directions: data-level and algorithm-level [14; 15]. Data-level approaches include oversampling, undersampling, and hybrid sampling, while algorithm-level approaches involve ensemble learning and cost-sensitive algorithms. Specifically, they are as follows:  
(1) Undersampling Methods: Undersampling methods begin with the majority class data and reduce the dataset by removing the class with a higher quantity to balance the dataset. For example, [PERSON] et al. [16] started by eliminating duplicate samples and expanded the detection range of the Tomek-link undersampling algorithm by introducing a global re-labeling index. [PERSON] et al. [17], focusing on data complexity, proposed an undersampling algorithm based on weighted complexity, WCP-UnderSampler, which achieved promising results in defect prediction datasets. However, undersampling can lead to information loss in the majority class and affect classifier generalization. (2) Oversampling Methods: Oversampling methods introduce new minority class data to create a superset of minority class samples, reducing the imbalance between data categories [18]. For instance, SMOTE is a classical oversampling algorithm for addressing imbalanced data [19]. [PERSON] et al. [20] proposed an improved SMOTE oversampling algorithm based on ant clustering, addressing both inter-cluster and intra-cluster data imbalance. [PERSON] et al. [21] introduced genetic chromosome theory into the sampling domain, presenting the MAHAKIL algorithm, which ensures that the generated data inherit the features of parent data instances. It outperforms other oversampling methods on multiple datasets.