Traffic Congestion

Traffic congestion research aims to understand and mitigate the negative impacts of traffic bottlenecks, focusing on accurate prediction and effective management strategies. Current research employs diverse machine learning models, including deep learning architectures like convolutional encoder-decoders, graph neural networks, and recurrent neural networks (RNNs, such as LSTMs), to analyze large-scale traffic datasets and predict congestion patterns at various granularities (e.g., individual intersections, road segments, or entire urban areas). These advancements offer the potential for improved urban planning, optimized traffic control systems, and more efficient transportation networks, ultimately reducing commute times, fuel consumption, and emissions.