## 1 Introduction
### Related Works  
To date, many models and algorithms have been used in the prediction of traffic accidents by researchers. Real-time traffic flow and crash data are difficult to obtain, so simulation and regression analysis are often used to simulate traffic crashes [3]. The development of big data and machine learning has led to an increasing number of models being used in traffic accident prediction, typically Bayesian networks, artificial neural networks, support vector machines, decision tree models, and random forests [4; 5; 6; 7]. [PERSON], [PERSON] et al. [8] used statistical analysis of tunnel accident data from the Shanghai Center 110 to determine the spatial and temporal distribution of traffic accidents in the Shanghai Crossing Tunnel. [PERSON] [9] used the Logistic Regression Model to investigate the data on traffic accidents in Saudi Arabia. [PERSON] et al. [10] used the data mining method of correlation analysis to study traffic accidents without intersections in Florida, and the correlation between traffic impact factors was analyzed. [PERSON] et al. [11] studied the prediction of short-term crossover speed and travel time between three theoretical models of the grey system. [PERSON] et al. [12] used crash prediction risk assessment models to evaluate the wind hazard of future serious vehicle crashes on the road network in order to reduce the likelihood of severe traffic congestion. For example, [PERSON] et al. [13] took Dutch data on traffic crashes as a sample, selected influential factors from crash data, filtered factors using a combination of master cost analysis and stepwise regression analysis, then set up the Bayesian network using WEKA to model and test at an 8:1 ratio. [PERSON] et al. [2] compared ANN with the random forest model and showed ANN to be simpler and more effective than the ANN model. [PERSON] et al. [14] studied the use of three advanced deep learning methods to model the severity of motor vehicle collision injuries, namely, Keras's General Multilayer Sensor (MLP), MLP with embedded layers, and TabNet. Among them, TabNet is an attention-based spreadsheet data network. Experimental results show that all the models produce a similar and sufficient performance based on most evaluation indices. [PERSON] and [PERSON] [15] developed a hybrid genetic algorithm ANN model for predicting the length of crashes on Taiwan's freeways. Although the accuracy of the report was high (R\({}^{2}\) = 0.94), their dataset was quite limited (only 39 records), and the corresponding events were only between 30 and 133 min long. In another interesting study, [PERSON] et al. [16] compared five parametric and nonparametric models using datasets from two sections of Italian motorways. It used 237 event datasets and all the models it tried achieved acceptable performance on the dataset with a small range of event durations. [PERSON] et al. [17] used stacked ML techniques, including RF, to predict categories of crash severity, and the authors used data from approximately 5500 crashes that occurred in approximately 320 different regions of highway in Florida. To calibrate their model, the authors refined several parameters, such as the number of trees, learning rates, and regularization factors. [PERSON] et al. [18] built a crash prediction model that was developed for an urban freeway in Santiago, Chile. The purpose of this study was to use RF models to identify the strongest warning signs of an accident. [PERSON] [19] used traditional taxonomy decision trees to predict duration categories (abbreviated, medium term, and long term) of the approximately 4500 traffic accidents that took place in Taiwan in 2008. [PERSON] et al. [20] have developed a hybrid model that combines M5P trees with hazard duration models based on parameters to predict the duration of crashes.