{"title":"用于城市轨道交通高峰时段客流预测的多序列时空特征融合网络","authors":"Lining Liu, Yugang Liu, Xiaofei Ye","doi":"10.1080/19427867.2024.2327805","DOIUrl":null,"url":null,"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 decompositi...","PeriodicalId":501080,"journal":{"name":"Transportation Letters","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":\"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 decompositi...\",\"PeriodicalId\":501080,\"journal\":{\"name\":\"Transportation Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19427867.2024.2327805\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19427867.2024.2327805","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-sequence spatio-temporal feature fusion network for peak-hour passenger flow prediction in urban rail transit
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 decompositi...
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