多维兴趣点驱动的地铁客流多通道集成预测方法OA

Points of Interests(POIs)within the service radius of metro stations embody diverse spatiotemporal characteristics and play a crucial role in shaping passenger mobility.During specific periods,POIs attract substantial interactive passenger flows.To accurately capture the mechanisms through which POIs influence metro ridership,this study proposed a multidimensional POI-driven multi-channel integrated prediction framework for metro passenger flow.First,this paper had comprehensively considered three cases.(I)the attraction of station attributes on passenger flow,(II)the dynamic variations in POI attractiveness across different time periods,and(III)the distribution and proximity of POIs within each station's service area.It constructed a station passenger attraction function,a POI temporal weighting model,and a POI spatial interaction matrix.Subsequently,this paper developed a Multi-Channel Attention Spatio-Temporal Neural Network(MCASTNN)that adopted a three-branch architecture and incorporates multiple attention mechanisms to mine the deep correlations between multidimensional POIs and metro ridership.A multi-channel attention integration module was then employed to fuse features,enabling the model to effectively capture complex station,temporal,and spatial dependencies in metro passenger flow.Finally,experiments were conducted using Hangzhou metro AFC smart card data.The results demonstrate that,compared with classical machine learning and deep learning models,MCASTNN can achieve superior prediction accuracy across residential-dominated,work-dominated,and mixed-commercial stations under various forecasting horizons.Specifically,relative to the Transformer model,MCASTNN can reduce the root mean square error and mean absolute error in single-step prediction tasks by an average of 3.09 and 3.11,respectively.Ablation studies on POI features and model components further verify the effectiveness of the proposed framework and each feature extraction branch.This approach can provide a valuable reference for optimizing the allocation of metro station resources and formulating rational train operation strategies.