Md Hishamur Rahman, Masnun Abrar, Shakil Mohammad Rifaat
{"title":"线性回归耦合瓦瑟斯坦生成对抗网络，用于芝加哥和奥斯汀的打车出行直接需求建模","authors":"Md Hishamur Rahman, Masnun Abrar, Shakil Mohammad Rifaat","doi":"10.1080/19427867.2024.2372944","DOIUrl":null,"url":null,"abstract":"Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the...","PeriodicalId":501080,"journal":{"name":"Transportation Letters","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin\",\"authors\":\"Md Hishamur Rahman, Masnun Abrar, Shakil Mohammad Rifaat\",\"doi\":\"10.1080/19427867.2024.2372944\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the...\",\"PeriodicalId\":501080,\"journal\":{\"name\":\"Transportation Letters\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-06\",\"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/10.1080/19427867.2024.2372944\",\"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/10.1080/19427867.2024.2372944","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin
Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the...
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
