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In this paper, in order to solve this challenge, the Bi-graph Graph Convolutional Spatio-Temporal Feature Fusion Network (BGCSTFFN)-based model is introduced to capture complex spatio-temporal correlations. A combination of a graph convolutional neural network and a Transformer is used. The model separately inputs land use (point of interest, POI) and station adjacency information as features into the BGCSTFFN model, using the Pearson correlation coefficient matrix, which is evaluated on real passenger flow dataset from 1 to 25 January 2019 in Hangzhou. The results showed that the model consistently provided the best prediction results across different datasets and prediction tasks compared to other baseline models. In addition, in tasks involving predictions with different combinations of inputs and prediction steps, the model showed superior performance at multiple prediction steps. Its practical application is validated by comparing the results of passenger flow prediction for different types of stations. In addition, the impact of these features on the prediction accuracy and the generalization ability of the model were verified by designing ablation experiments and testing on different datasets.<\/jats:p>","DOI":"10.3390\/systems13020096","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T05:55:22Z","timestamp":1738648522000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Jianan","family":"Sun","sequence":"first","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-4955","authenticated-orcid":false,"given":"Xiaofei","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Road #2, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0858-1482","authenticated-orcid":false,"given":"Xingchen","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-3712","authenticated-orcid":false,"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3846\/16484142.2011.555472","article-title":"The use of Ls-SVM for short-term passenger flow prediction","volume":"26","author":"Chen","year":"2011","journal-title":"Transport"},{"key":"ref_2","first-page":"1256","article-title":"Passenger Flow Prediction Based on Particle Filter Optimization","volume":"373\u2013375","author":"Cao","year":"2013","journal-title":"Mechatron. 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