{"title":"基于相关性的特征选择和并行时空网络用于地铁系统的高效客流预测","authors":"Cong Xiu, Shuguang Zhan, Jinyi Pan, Qiyuan Peng, Zhiyuan Lin, S.C. Wong","doi":"10.1080/23249935.2024.2335244","DOIUrl":null,"url":null,"abstract":"This paper presents a novel framework for predicting metro passenger flow that is both interpretable and computationally efficient. The proposed method first uses a correlation-based spatiotemporal...","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"26 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correlation-based feature selection and parallel spatiotemporal networks for efficient passenger flow forecasting in metro systems\",\"authors\":\"Cong Xiu, Shuguang Zhan, Jinyi Pan, Qiyuan Peng, Zhiyuan Lin, S.C. Wong\",\"doi\":\"10.1080/23249935.2024.2335244\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel framework for predicting metro passenger flow that is both interpretable and computationally efficient. The proposed method first uses a correlation-based spatiotemporal...\",\"PeriodicalId\":48871,\"journal\":{\"name\":\"Transportmetrica A-Transport Science\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportmetrica A-Transport Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/23249935.2024.2335244\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportmetrica A-Transport Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/23249935.2024.2335244","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Correlation-based feature selection and parallel spatiotemporal networks for efficient passenger flow forecasting in metro systems
This paper presents a novel framework for predicting metro passenger flow that is both interpretable and computationally efficient. The proposed method first uses a correlation-based spatiotemporal...
期刊介绍：
Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.
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