Optimizing Spatial Node-Node Connections for New and Existing Metro Interchange Stations Through Traffic Spatiotemporal Coupling Analysis

The rapid expansion of urban metro networks often makes interchange stations critical bottlenecks, especially when new lines connect to existing infrastructure. Passenger flows converge in confined spaces, yet the spatiotemporal dynamics of such interactions remain underexplored. This study presents a simulation-based framework that couples time-varying passenger demand with spatial network constraints, implemented in AnyLogic to model pedestrian behavior and transfer node performance. Using Guangzhou Fangcun Station—where Line 1 meets new Lines 11 and 22—as a case study, we evaluate multiple design scenarios based on Fruin's Level of Service (LOS) criteria. Results show that integrating two new lines without targeted node adjustments drives key transfer points into LOS E–F under peak loads, indicating severe congestion. Two remedial measures—widening corridors and adding platform-to-platform connections—are subsequently tested. These interventions improve peak LOS to C–D, significantly alleviating congestion at previously overloaded nodes. Unlike conventional LOS assessments that treat demand and capacity separately, our coupled framework captures how temporal surges propagate through spatial bottlenecks, offering a more realistic diagnostic approach for complex interchanges. The findings provide practical guidance for node design optimization and a methodological reference for transfer capacity evaluation in high-density rail networks.