Passenger Flow Prediction

Passenger flow prediction aims to accurately forecast the movement of people within transportation systems, primarily to optimize resource allocation and improve operational efficiency. Current research heavily utilizes deep learning models, such as Long Short-Term Memory networks (LSTMs), Graph Convolutional Networks (GCNs), and Transformers, often incorporating multi-task learning and generative adversarial networks (GANs) to capture complex spatiotemporal dependencies and account for external factors like weather and events. These advancements are crucial for improving the management of urban transit systems, enhancing passenger experience, and enabling more effective real-time scheduling and resource deployment.