Urban Traffic Inference using Spatial Temporal Frameworks

Traffic congestion continues to be a serious urban problem and this problem motivates research in predictive modeling and optimization. Advancements in machine learning and deep learning, coupled with Internet of Things (IoT)-enabled real time monitoring, have improved the prediction of traffic flow. The incorporation of multi-modal data into graph neural networks has further enabled representations and scalable management of urban traffic with higher accuracy.