<p>The number of motor vehicles have experienced significant growth over the past few decades as the economy continues to grow and urbanization accelerates. This phenomenon creates an urgent need for smart transportation technology. This paper aims to explore a prediction method suitable for predicting traffic flow in public transportation systems and the challenge of coping with the impact of abnormal traffic events on prediction. To this end, we propose a deep learning approach using dynamic graph networks and multi-head attention mechanisms to develop a traffic flow prediction method based on spatio-temporal multi-source information fusion. In addition, this paper also proposes an urban road traffic prediction method suitable for normal and abnormal traffic conditions. This method employs a deep learning framework to process traffic and accident data through dynamic graph networks and multi-task learning. The experimental results on three real traffic datasets, namely PEMS04, PEMS08 and Highways England, show that the proposed model has significantly better MAE than the optimal baseline AGCRN on PEMS04. On the England dataset, RMSE was 18.7% lower than DCRNN. In terms of long-term prediction, the MAE predicted by the model at 60&#xa0;min is still lower than the baseline. Extensive experiments on a real-world dataset validate the effectiveness of our model. This study provides a novel perspective and solution for smart city traffic prediction, while developing a predictive tool for abnormal events, thereby enhancing the intelligence level of traffic management.</p>