The short term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management. The emerging deep learning-based models provide effective methods to improve prediction accuracy. However, most of the existing models mainly predict the passenger flow on general weekdays or weekends. There are only few studies focusing on predicting the passenger flow on holidays, which is a significantly challenging task for traffic management because of its suddenness and irregularity. To this end, we propose a deep learning-based model named Spatial Temporal Attention Fusion Network comprising a novel Multi-Graph Attention Network, a Conv-Attention Block, and Feature Fusion Block for short-term passenger flow prediction on holidays. The multi-graph attention network is applied to extract the complex spatial dependencies of passenger flow dynamically and the conv-attention block is applied to extract the temporal dependencies of passenger flow from global and local perspectives. Moreover, in addition to the historical passenger flow data, the social media data, which has been proven that they can effectively reflect the evolution trend of passenger flow under events, are also fused into the feature fusion block of STAFN. The STAFN is tested on two large-scale urban rail transit AFC datasets from China on the New Year holiday, and the prediction performance of the model are compared with that of several conventional prediction models. Results demonstrate its better robustness and advantages among benchmark methods, which can provide overwhelming support for practical applications of short term passenger flow prediction on holidays.

翻译：城市轨道交通系统的短期客流预测对交通运营与管理具有重要意义。基于深度学习的新兴模型为提高预测精度提供了有效方法。然而，现有模型主要预测普通工作日或周末的客流量，而针对节假日客流预测的研究较少。由于节假日的突发性和不规则性，这一任务对交通管理极具挑战性。为此，我们提出一种基于深度学习的模型——时空注意力融合网络（Spatial Temporal Attention Fusion Network，STAFN），该模型包含新型多图注意力网络（Multi-Graph Attention Network）、卷积注意力模块（Conv-Attention Block）和特征融合模块（Feature Fusion Block），用于节假日短期客流预测。多图注意力网络用于动态提取客流复杂的空间依赖性，卷积注意力模块则从全局和局部视角提取客流的时间依赖性。此外，除历史客流数据外，STAFN的特征融合模块还融合了社交媒体数据——已证明此类数据能有效反映事件下客流演变趋势。该模型在中国两个城市轨道交通自动售检票系统（AFC）的新年节假日大规模数据集上进行了测试，并将其预测性能与多种传统预测模型进行对比。结果表明，STAFN在基准方法中展现出更优的鲁棒性和优势，可为节假日短期客流预测的实际应用提供有力支持。