Urban Rail Transit

Urban rail transit research centers on improving the efficiency and effectiveness of these crucial transportation systems. Current efforts focus on developing sophisticated predictive models, often employing machine learning techniques like graph neural networks, Bayesian networks, and transformers, to forecast passenger demand and optimize energy consumption under various operational conditions and external factors such as weather and pandemics. These advancements aim to enhance resource allocation, improve scheduling, and ultimately increase the sustainability and reliability of urban rail networks. The resulting improvements in prediction accuracy and operational efficiency have significant implications for urban planning, transportation management, and the overall passenger experience.