Origin Destination

Origin-destination (OD) flow prediction aims to accurately forecast the movement of people or vehicles between different locations, crucial for optimizing urban transportation and planning. Current research heavily focuses on developing sophisticated machine learning models, including graph neural networks, transformers, and large language models, to capture complex spatiotemporal dependencies in OD data and handle large-scale networks. These advancements improve the accuracy and efficiency of OD estimations, leading to better traffic management, resource allocation, and urban infrastructure design. The ability to accurately predict OD flows has significant implications for improving urban mobility, reducing congestion, and enhancing the overall efficiency of transportation systems.