Multi-sequence spatio-temporal feature fusion network for peak-hour passenger flow prediction in urban rail transit

Abstract

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.