Traffic flow prediction using adaptive graph convolutional networks and long short-term memory

Traffic congestion is becoming an increasingly serious and challenging issue in major urban areas. This problem not only causes a waste of time and increased fuel consumption but also contributes to environmental pollution and deterioration of residents’ quality of life. In this study, a new method of predicting the average speed reported by traffic sensors across the city was proposed. In this method, we make the most of two core models: Graph Convolutional Networks and Long Short-Term Memory. The YOLO model is used to analyze images and video during data collection. By leveraging Graphe Convolution Networks ability to capture spatial information, Long Short-Term Memory capacity to model temporal dynamics, and YOLO’s strength in visual object detection, our integrated framework enhances the accuracy of traffic flow predictions at specific locations and time intervals. This comprehensive approach aims to support real-world applications such as adaptive traffic light control, traffic planning support, and congestion alerts. The proposed method outperforms other methods on the Caltrans PeMS dataset.