Spatiotemporal Evolution Patterns and Intelligent Forecasting of Passenger Flow in Megacity High-Speed Rail Hubs: A Case Study of Guangzhou South Railway Station

Flow in high-speed rail (HSR) hubs serves as a “barometer” for factor mobility within urban agglomerations, and its accurate forecasting is crucial for capacity allocation and emergency management. This paper focuses on two core aspects: passenger flow characterization and intelligent forecasting methodology. Taking Guangzhou South Railway Station (GSRS) as a typical case, it utilizes multi-source big data to deeply excavate the refined spatiotemporal distribution patterns and structural characteristics of hub passenger flow. Furthermore, a hybrid VMD-CNN-BiLSTM-Attention-XGBoost forecasting model integrating time series decomposition, deep learning, and ensemble learning is constructed. The study finds that passenger flow exhibits a pattern of “dual peaks on weekdays for commuting and a single peak on weekends for leisure”, with the Shenzhen/ Hong Kong SAR direction accounting for over 30%. The constructed hybrid model demonstrates significantly superior forecasting accuracy (MAPE = 3.76%) compared to benchmark models. This research provides methodological and decision-making support for the transition of mega HSR hubs from “experience-based operation” to “data-driven” precise governance.