Intelligent transportation systems based on sensor signals are crucial in addressing contemporary transportation issues, accomplishing dynamic traffic management, and facilitating route planning. However, the highly dynamic and intricate nature of traffic sensor signals presents difficulties for traffic prediction, with current models for traffic prediction inadequate in meeting the requirements of both long-term and short-term prediction tasks. In this paper, we propose a novel deep-learning framework called dynamic spatial-temporal correlation learning network (DSTLNet) that jointly leverages dynamical spatial and temporal features of traffic sensor signals to further improve the accuracy of long- and short-term traffic modeling and route planning. Specifically, we leverage the temporal convolutional network to capture long-term correlations. In addition, a spatial graph convolutional network is developed to dynamically model spatial features, and long- and short-term fusion layers are used to fuse the extracted long- and short-term temporal features, respectively. Experimental results on real-world datasets show that DSTLNet is competitive with the state-of-the-art, especially for long-term traffic prediction.