Passenger Flow

Passenger flow analysis focuses on understanding and predicting the movement of people within transportation systems, aiming to optimize efficiency and resource allocation. Current research emphasizes the use of advanced machine learning techniques, including graph neural networks, transformers, and large language models, to analyze complex spatiotemporal data and predict passenger behavior under various conditions, such as delays or holidays. These predictive models are crucial for improving transportation planning, managing disruptions, and enhancing the overall passenger experience, with applications ranging from optimizing airport security checkpoints to improving urban rail transit scheduling. The integration of diverse data sources, such as geolocation data and social media, further enhances the accuracy and applicability of these models.