## 4 Conclusions  
Short-term traffic flow prediction is essential for modern traffic management and urban planning. It helps optimize traffic control, prevent accidents, support intelligent transportation systems, and enhance economic efficiency. Existing studies primarily focus on short-term traffic flow prediction for main roads. However, they overlook issues such as the lower traffic volume and prediction accuracy on side roads. To address these limitations, this paper proposes a prediction model that combines association rules with SVR. In  
Figure 16: Comparison of SVR modeled feeder road tests. addition, this paper also expands the index system by reading a lot of literature, such as adding the weather and other indicators. In contrast to alternative forecasting models, the model developed herein is more effective, and the proposed model demonstrates improved performance, as evidenced by lower RMSE and MAE values, and meets the needs of short-term traffic flow prediction. The conclusions of this paper are as follows:  
1. First, Apriori correlation analysis is carried out on 20 roads with 80% support and 90% confidence level. Through this analysis, the frequent term set of each road, i.e., the relevant road of each road, is obtained. 2. After road correlation for each road section, the SVR model with penalty parameter C of 1.2466 and kernel parameter gamma of 0.8327 is established based on the short-term traffic flow data of the target road and the related roads as a database. The model predicts the related roads more accurately compared with the real ones. 3. Several prediction algorithms are used to compare the performance with this paper's algorithm, by comparing the root mean square error, the average absolute error, and the average relative error of each model, and the proposed SVR model achieved an RMSE of 11.422 and an MAE of 7.017, while the LSTM model had an RMSE of 14.5 and an MAE of 8.2, the Random Forest model had an RMSE of 13.8 and an MAE of 7.8, and the GBDT model had an RMSE of 13.2 and an MAE of 7.5. It can be seen that the proposed model outperforms other models in terms of RMSE, MAE, and MRE, demonstrating superior prediction accuracy and fitting effect. However, it should be pointed out that there are also areas for improvement in this paper. In particular, in terms of constructing an indicator system, although this paper summarizes an indicator system based on a large amount of literature, there is still much room for improvement and enrichment. Considering their potential exacerbation of urban traffic congestion, this study incorporates horizontal visibility and total cloudiness into the short-term traffic volume forecasting index system of Xi'an to improve the accuracy and practicality of the prediction model. However, there are other factors that could further enrich the indicator system and enhance its practicality. For example, population density, local events, and traffic patterns in commercial areas are all factors that can significantly impact traffic flow. Future work could explore the inclusion of these factors in the indicator system to provide a more comprehensive and accurate prediction model. In order to solve this problem, the considered improvements are as follows:  
1. Indicator expansion: 1 Population density: Incorporating population density data could help in understanding the impact of residential and commercial areas on traffic flow. Areas with higher population density are likely to experience higher traffic volumes, especially during peak hours. 2 Local events: Special events such as concerts, sports games, and festivals can cause significant changes in traffic patterns. Including data on local events could help in predicting traffic congestion more accurately. 3 Traffic patterns in commercial areas: Commercial areas often have different traffic patterns compared to residential areas. Understanding these patterns and incorporating them into the model could improve the prediction accuracy in urban settings. 2.