AI spatio-temporal prediction of industrial heritage land-use influenced by dynamic passenger flow at metro stations

Abstract 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, we developed a dynamic passenger flow-oriented land use prediction model of subway stations. 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, our model accurately simulated pedestrian flow density from stations to streets. We conducted experiments to predict inefficient land-use scenarios and compared them 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.