## 3 Methodology
### Dataset
#### 3.1.2 Duplicate Value Handling  
From the perspective of the features in the US-Accidents traffic accident dataset, there are two sources of duplicate values in the dataset:  
1. Repetitive reporting of the same traffic accident information by different reporting sources. For the same traffic accident, different reporting sources may report it, and differences in the source can also result in variations in geographic and time information. Based on the different data sources, the source field is categorized into Source 1: reported from Bing, Source 2: reported from MapQuest, and Source 3: reported from both parties;
2. Multiple reports of information caused by the same traffic accident source. This is because the determination of traffic accident types in US-Accidents is based on whether traffic delays occur in a specific geographical location, and there is a strong spatial correlation between adjacent roads. Therefore, closely located traffic congestion may have a chain reaction, leading to multiple reports of traffic congestion on closely located roads caused by the same traffic accident source.  
Given the two mentioned reasons, to ensure minimal and accurate duplicate data in the experiment's dataset, while also avoiding data leakage due to duplicate values, we excluded the 2016 data that had overall fewer records and a substantial disparity in data volume between different months. We retained data from 2017 to 2022 for the experiment. Subsequently, we screened the remaining dataset of over six million entries for duplicate values, filtering out entries with a time interval smaller than 10 min and a geographical distance less than 250 m, assessed by the geographical location formula illustrated in Equation (2). Here, \(\varphi_{a}\) and \(\varphi_{b}\) represent the longitudes of points a and b, respectively, while \(\lambda_{a}\) and \(\lambda_{b}\) denote the latitudes of points a and b, respectively.  
\[\text{dist}(\text{a},\text{b})=2\text{arcsin}(\sqrt{\sin^{2}\frac{(\varphi_{a }-\varphi_{b})}{2}+\cos(\varphi_{a})\cos(\varphi_{b})\sin^{2}\frac{(\lambda_{ a}-\lambda_{b})}{2}}) \tag{2}\]  
To provide a more detailed demonstration of the process of eliminating duplicate data based on time and distance, we will illustrate it through the following example (Table 4):  
Assuming that the above four data are subjected to repeat value judgment, the data pairs (X\({}_{1}\), X\({}_{3}\)), (X\({}_{1}\), X\({}_{4}\)), and (X\({}_{2}\), X\({}_{3}\)) with time interval less than 10 min are firstly identified. Then, the longitude and latitude values of the above data pairs are substituted into Equation (2) for distance judgment, which yields \(\text{dist}(\text{X}_{1}\), X\({}_{3}\)) = 129,461.41 m, \(\text{dist}(\text{X}_{1}\), X\({}_{4}\)) = 240.38 m, and \(\text{dist}(\text{X}_{2}\), X\({}_{3}\)) = 179,192.03 m. Therefore, we believe that the duplicated point may be between X\({}_{1}\) and X\({}_{4}\), and that X\({}_{1}\) occurs earlier than X\({}_{4}\), therefore, X\({}_{1}\) is the real place where the accident originated.  
In this section, we screened and deleted duplicate values from 2017 to 2022, and deleted duplicate value data with time intervals of less than 10 min and geographic locations of less than 250 m to ensure the purity of the data and to avoid data leakage.