{"title":"利用结构化数据和非结构化文本日志预测地铁事故持续时间","authors":"Yangyang Zhao, Zhenliang Ma, Hui Peng, Zhanhong Cheng","doi":"10.1080/23249935.2024.2396951","DOIUrl":null,"url":null,"abstract":"Predicting metro incident duration is crucial for passengers and transit operators to choose appropriate response strategies. Most existing research focuses on structured data, the rich information...","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"2 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting metro incident duration using structured data and unstructured text logs\",\"authors\":\"Yangyang Zhao, Zhenliang Ma, Hui Peng, Zhanhong Cheng\",\"doi\":\"10.1080/23249935.2024.2396951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting metro incident duration is crucial for passengers and transit operators to choose appropriate response strategies. Most existing research focuses on structured data, the rich information...\",\"PeriodicalId\":48871,\"journal\":{\"name\":\"Transportmetrica A-Transport Science\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-08-29\",\"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/10.1080/23249935.2024.2396951\",\"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/10.1080/23249935.2024.2396951","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Predicting metro incident duration using structured data and unstructured text logs
Predicting metro incident duration is crucial for passengers and transit operators to choose appropriate response strategies. Most existing research focuses on structured data, the rich information...
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
