Travel Demand

Travel demand modeling aims to predict the movement of people and goods across transportation networks, supporting efficient resource allocation and infrastructure planning. Current research heavily utilizes machine learning, particularly deep learning architectures like graph convolutional networks, recurrent neural networks, and transformers, to capture complex spatiotemporal patterns and integrate diverse data sources (e.g., demographics, land use, real-time mobility data). This field is crucial for optimizing transportation systems, improving urban planning, and mitigating issues like congestion, overcrowding, and inequitable service distribution, with applications ranging from ride-sharing optimization to disaster response planning. Furthermore, there's a growing emphasis on incorporating fairness considerations and quantifying prediction uncertainty to enhance the reliability and societal impact of travel demand models.