Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis

Short-time traffic flow prediction is essential for intelligent traffic management. By accurately predicting traffic conditions in the near future, it helps to alleviate congestion, improve road efficiency, reduce accidents, and support timely traffic control. Short-time traffic flow exhibits uncertainty and randomness, and this paper proposes an SVR model for short-time traffic flow prediction on non-main and branch roads, using correlations between associated roads to improve accuracy. Association Rule Analysis: First, we use Pearson correlation to identify strongly correlated roads. This step helps in understanding the relationships between different roads and their traffic patterns. SVR Model Construction: Second, based on the identified correlations, we construct an SVR model using traffic data from the target road and its associated roads. The model parameters are optimized using grid search and cross-validation to ensure the best performance. Simulation and Evaluation: Third, we conduct simulation experiments using real traffic data from Xi’an city. The performance of our model is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE). Simulation experiments show that our model outperforms existing methods. Specifically, our model achieved an RMSE of 11.422, an MAE of 7.017, and an MRE of 0.139. In comparison, other models tested in our study, such as LSTM, Random Forest, and Gradient Boosting Decision Tree (GBDT), had higher error values. For instance, the LSTM model had an RMSE of 14.5, an MAE of 8.2, and an MRE of 0.165; the Random Forest model had an RMSE of 13.8, an MAE of 7.8, and an MRE of 0.152; and the GBDT model had an RMSE of 13.2, an MAE of 7.5, and an MRE of 0.148. These results demonstrate that our proposed SVR model, combined with association rules, is highly effective in predicting short-time traffic flow on non-main and branch roads, which are often overlooked in existing research.