{"title":"利用自适应灰色预测区间模型量化实时交通状况的不确定性","authors":"Zhanguo Song, Xinran Wang, Wei Huang, Meiye Li, Xiaobin Zhong, Jianhua Guo","doi":"10.1080/23249935.2024.2394522","DOIUrl":null,"url":null,"abstract":"Uncertainty quantification is important for making reliable transportation decisions. For grey-based uncertainty quantification approaches, the data classification methods for most models cannot yi...","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"14 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time traffic condition uncertainty quantification using adaptive grey prediction interval model\",\"authors\":\"Zhanguo Song, Xinran Wang, Wei Huang, Meiye Li, Xiaobin Zhong, Jianhua Guo\",\"doi\":\"10.1080/23249935.2024.2394522\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Uncertainty quantification is important for making reliable transportation decisions. For grey-based uncertainty quantification approaches, the data classification methods for most models cannot yi...\",\"PeriodicalId\":48871,\"journal\":{\"name\":\"Transportmetrica A-Transport Science\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-08-26\",\"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/10.1080/23249935.2024.2394522\",\"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/10.1080/23249935.2024.2394522","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Real-time traffic condition uncertainty quantification using adaptive grey prediction interval model
Uncertainty quantification is important for making reliable transportation decisions. For grey-based uncertainty quantification approaches, the data classification methods for most models cannot yi...
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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