Spatiotemporal Prediction of the Impact of Dynamic Passenger Flow at Subway Stations on the Sustainable Industrial Heritage Land Use

Inefficient land reuse has emerged as a critical pathway for the sustainable development of urban spaces. Efficient land development in megacities’ industrial heritage areas is heavily influenced by the influx of mass passenger flows from new subway stations. To address this issue, a dynamic passenger flow-oriented land use prediction model for subway stations was developed. This model iterates a simulation model for dynamic passenger flow based on tourists and residents with an artificial neural network for land use prediction. By enhancing the kappa coefficient to 0.86, the model accurately simulated pedestrian flow density from stations to streets. Experiments were conducted to predict inefficient land use scenarios, which were then compared with the current state in national industrial heritage areas. The results demonstrated that the AnyLogic-Markov-FLUS Coupled Model outperformed expert experience in objectively assessing dynamic passenger flow impacts on the carrying capacity of old city neighborhoods during peak and off-peak periods at subway stations. This model can assist in resilient urban space planning and decision-making regarding mixed land use.