{"title":"Multi-sequence spatio-temporal feature fusion network for peak-hour passenger flow prediction in urban rail transit","authors":"Lining Liu , Yugang Liu , Xiaofei Ye","doi":"10.1080/19427867.2024.2327805","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 1","pages":"Pages 86-102"},"PeriodicalIF":3.3000,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Letters-The International Journal of Transportation Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S194278672400016X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Transportation Letters: The International Journal of Transportation Research is a quarterly journal that publishes high-quality peer-reviewed and mini-review papers as well as technical notes and book reviews on the state-of-the-art in transportation research.
The focus of Transportation Letters is on analytical and empirical findings, methodological papers, and theoretical and conceptual insights across all areas of research. Review resource papers that merge descriptions of the state-of-the-art with innovative and new methodological, theoretical, and conceptual insights spanning all areas of transportation research are invited and of particular interest.