随着城市化的快速发展，地铁作为公共交通的重要组成部分，在缓解交通压力和改善交通环境方面扮演着重要角色。因此，精确预测地铁客流量对于地铁运营管理、资源优化配置和应急事件处理具有重要意义。由于地铁站点和线路间存在复杂关联性，加之地铁运营数据的大规模和高维度特性，现有方法虽然考虑了多个站点和线路，但未能充分考虑站点间的高阶关系以及深入探索乘客在地铁复杂图结构中隐含的演变规律，导致预测准确性有限。因此，本文提出了一个新的模型——地铁客流时空特征集成网络（STIN），从多维度、多层次的角度全面捕捉地铁系统中站点和线路间的复杂关联性，旨在提高地铁OD客流量的短时预测精度。该模型结合了物理网络图、相似性网络图、行为相关性网络图的特点，以及基础和高级超图，能够从多维度捕捉地铁系统中站点间的复杂关系。特别是，超图卷积部分通过考虑站点间的高阶依赖性，为理解乘客流动模式和站点间动态变化提供了新的视角。本研究在杭州2019年1个月27天的地铁数据集上进行了实验，涉及3条地铁线路和80个站点。实验结果显示，STIN在预测准确性方面优于传统GCN、STCGN、ASTGCN模型。总体而言，本研究展示了深度学习技术在地铁客流量预测领域的应用潜力，为地铁运营管理和城市交通的智能化提供了新的方向。

As urbanization accelerates, the metro has become a crucial part of public transport, essential for alleviating urban traffic pressure and enhancing transportation environments. Accurate prediction of metro passenger flow is significantly valuable for metro operation management, resource optimization, and emergency response. Due to the complex correlations between metro stations and lines, coupled with the large-scale and high-dimensional nature of metro operational data, existing methods, while considering multiple stations and lines, fail to adequately capture their interactions and dependencies in terms of passenger mobility patterns, time, and spatial distributions, resulting in limited prediction accuracy. Therefore, this paper proposes a new model, the Spatio-Temporal Characteristics Integrated Network (STIN) for Metro Passenger Flow, to comprehensively capture the complex correlations between stations and lines in the metro system from a multi-dimensional and multi-level perspective, aiming to improve the accuracy of short-term prediction of metro OD passenger flow. STIN harnesses physical network graphs, similarity network graphs, behavioral relevance network graphs, and both basic and advanced hypergraphs to capture the complex relationships among metro stations comprehensively. The model particularly emphasizes the utilization of hypergraph convolution to consider higher-order dependencies between stations, providing a new perspective on passenger flow patterns and station-to-station dynamics. Tested on a dataset from Hangzhou's metro system over 27 days in 2019, involving three metro lines and 80 stations, STIN demonstrated superior prediction accuracy compared to traditional models such as GCN, STGCN, and ASTGCN. The efficacy of STIN is attributed to its ability to effectively process complex and dynamic metro network data, ensuring high accuracy in real-time passenger flow predictions. This model can be extensively applied to urban traffic management, passenger flow forecasting, and optimization of metro operation schedules, significantly benefiting urban transport systems. While the current implementation of STIN relies on data from a specific region, its adaptability across different metro systems remains to be tested. Future research will aim to enhance the model's generalizability by incorporating diverse datasets and considering additional factors such as weather conditions, holidays, and seasonal variations. This work highlights the potential of deep learning techniques in advancing metro passenger flow prediction and underscores the importance of choosing the right model architecture and tuning parameters for time-series forecasting tasks.