所属单位：交通运输与物流学院
发表刊物：Transportation Letters: The International Journal of Transportation Research
摘要：This research addresses the challenge of predicting URT station passenger flow during peak hour. The Multi-Sequence Spatio-Temporal Feature Fusion Network Model (MSSTFFN) based on trend decomposition is introduced to capture complex spatio-temporal correlations. This model combines seasonal trend decomposition, graph convolutional neural networks, and modified Transformer networks. The MSSTFFN model is evaluated using actual data from Hangzhou City. The results indicate that, in comparison to the baseline model, this model consistently delivers the best prediction results across various datasets as well as prediction tasks. It exhibits exceptional and consistent performance in prediction sub-tasks involving different input and prediction step combinations, highlighting its advanced, robust, and versatile nature. Through micro-comparisons of specific prediction results for different types of stations, the practical application value is verified. Furthermore, through the design of ablation experiments and testing on various datasets, the contribution value of the features and model’s generalization capability are validated.
合写作者：Lining Liu, Yugang Liu, Xiaofei Ye
第一作者：Lining Liu
通讯作者：Yugang Liu
一级学科：交通运输工程
页面范围：1–17
是否译文：否