This paper presents a machine learning-based approach for traffic flow prediction and intelligent traffic management. The authors propose a Time-Graph Convolutional Network (T-GCN) model that combines graph convolutional networks and gated cycle units to capture both spatial and temporal dependencies in traffic data. The T-GCN model is designed to address the complex spatiotemporal characteristics of traffic flow, which are crucial for accurate predictions in urban traffic management. The paper highlights the challenges in traffic flow forecasting, such as the intricate coupling correlations between multivariate data and the dynamic spatio-temporal features. Traditional methods often struggle with these complexities, while deep learning models like CNNs and RNNs have shown promise in feature extraction. However, these models are not well-suited for graph data, leading to the development of GCNs. The T-GCN model is evaluated on two real-world datasets: the SZ-taxi dataset and the Los-loop dataset. The model's performance is assessed using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Accuracy. The results demonstrate that the T-GCN model outperforms other baseline methods in terms of prediction accuracy and spatio-temporal feature extraction. The model is particularly effective in capturing the changing trends of traffic data and detecting traffic congestion patterns. The paper concludes that the T-GCN model, based on machine learning convolutional models, offers significant advantages in traffic prediction, making it a valuable tool for intelligent transportation systems. The authors also acknowledge the contributions of previous research and express gratitude to their supporters.This paper presents a machine learning-based approach for traffic flow prediction and intelligent traffic management. The authors propose a Time-Graph Convolutional Network (T-GCN) model that combines graph convolutional networks and gated cycle units to capture both spatial and temporal dependencies in traffic data. The T-GCN model is designed to address the complex spatiotemporal characteristics of traffic flow, which are crucial for accurate predictions in urban traffic management. The paper highlights the challenges in traffic flow forecasting, such as the intricate coupling correlations between multivariate data and the dynamic spatio-temporal features. Traditional methods often struggle with these complexities, while deep learning models like CNNs and RNNs have shown promise in feature extraction. However, these models are not well-suited for graph data, leading to the development of GCNs. The T-GCN model is evaluated on two real-world datasets: the SZ-taxi dataset and the Los-loop dataset. The model's performance is assessed using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Accuracy. The results demonstrate that the T-GCN model outperforms other baseline methods in terms of prediction accuracy and spatio-temporal feature extraction. The model is particularly effective in capturing the changing trends of traffic data and detecting traffic congestion patterns. The paper concludes that the T-GCN model, based on machine learning convolutional models, offers significant advantages in traffic prediction, making it a valuable tool for intelligent transportation systems. The authors also acknowledge the contributions of previous research and express gratitude to their supporters.