## 3 Simulation Experiments
### Datasets
#### 3.1.2 Data Preprocessing  
After certain preprocessing of the data in the early stage, including supplementing the missing parts of the traffic flow data of each road in X city collected in the early stage according to the data type, eliminating the deficiencies in the traffic flow information collected in the early stage, removing the redundant information, and making appropriate adjustments according to the actual situation, better results were achieved. Generally speaking, various technical means such as data cleaning, data integration, and data conversion can be used [34]. 1. Missing value processing: Due to a variety of human and mechanical factors, the ordered raw data collected from traffic management information systems are often accompanied by serious deficiencies, a phenomenon that cannot be ignored and is often accompanied by serious consequences, for example, due to improper operation, damage to the collection equipment, and so on, which reduces the density of value of the raw data, thus bringing a negative impact on the forecast of traffic flow. The three most prevalent missing value filling techniques are deletion, patching, and neglect. 2. Data transformation: Through various data transformation methods, the data after initial processing are transformed into the data form needed by the model: firstly, through the smooth processing of the data, the noisy data in the initial data are eliminated, and they are excluded; after that, data clustering is carried out to summarize the various types of data, and the conceptual hierarchy is utilized to carry out a unified generalization of the various types of data, and then, the original data at the lower level are transformed into the higher level data. High-level data and, finally, through attribute construction, new attribute types are constructed, and these new attribute categories are incorporated into the attribute set. 3. Normalization of raw data: By normalizing the data, different orders of magnitude are finally classified as the same magnitude for more efficient system analysis and processing. This method makes raw data more dimensionless by scaling it by a certain percentage and confining it to a predetermined range. Data can be effectively normalized through data normalization, which can be in any range \([0,1]\). For the raw information \([0,1]\), the normalization process needs to be applied, i.e., it is compressed at a predefined rate so that it can be mapped to a specific range, and thus, the information within that range can be effectively used for systematic analysis. \[z=\frac{x_{i}-\min(x_{i})}{\max(x_{i})-\min(x_{i})} \tag{21}\]  
In the above equation, z denotes the normalized data, \(x_{i}\) denotes the associated eigenvalue, \(\min(x_{i})\) is the lowest point of the eigencolumn, and \(\max(x_{i})\) is the highest point of the eigencolumn. To verify the conclusions of the model, we randomly selected 10 main roads and 10 branch roads in Xi'an and processed the data with missing values. The specific selection of these roads is shown in Table 5:  
As for the data, a total of 80,640 data from 1 April 2022 to 7 April 2022 in spring time, including the Qingming Festival, 22 July 2022 to 29 July 2022 in summer time, including weekends, 6 October 2022 to 13 October 2022 in autumn time, including National Day Holiday, and 1 December 2022 to 8 December 2022 in winter time are selected as the predicted data of road traffic flow in this simulation experiment. According to the short-time traffic flow prediction index system established in this paper, the data preview of each index on 1 April 2022 is collected as shown in Table 6.