Public Transit

Public transit research centers on optimizing efficiency, accessibility, and resilience of urban transportation systems. Current efforts focus on developing sophisticated predictive models, often employing machine learning algorithms like neural networks (including graph convolutional and transformer architectures) and large language models, to forecast demand, manage disruptions, and improve route planning. These advancements aim to enhance both passenger experience and operational efficiency, impacting urban planning, resource allocation, and the development of sustainable transportation solutions. Furthermore, research emphasizes equity considerations, aiming to ensure fair and accessible transit for all socioeconomic groups.