Urban Transportation

Urban transportation research focuses on optimizing efficiency, resilience, and safety within increasingly complex multimodal systems. Current efforts leverage deep learning, particularly advanced architectures like transformer networks and long short-term memory models, to improve traffic forecasting, manage disruptions, and simulate the integration of autonomous vehicles and urban air mobility. These advancements aim to enhance the accuracy of demand prediction across various transport modes, optimize resource allocation during incidents, and inform policy decisions for sustainable and efficient urban planning. The resulting models and algorithms have significant implications for improving urban mobility and mitigating congestion.