{"title":"以轨迹数据为驱动的交通风险预测方法：纳入变量相互作用和预筛选","authors":"Dan Wu, Jaeyoung Lee, Ye Li","doi":"10.1080/12265934.2024.2346166","DOIUrl":null,"url":null,"abstract":"Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the ...","PeriodicalId":46464,"journal":{"name":"International Journal of Urban Sciences","volume":"24 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening\",\"authors\":\"Dan Wu, Jaeyoung Lee, Ye Li\",\"doi\":\"10.1080/12265934.2024.2346166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the ...\",\"PeriodicalId\":46464,\"journal\":{\"name\":\"International Journal of Urban Sciences\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Urban Sciences\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/12265934.2024.2346166\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Urban Sciences","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/12265934.2024.2346166","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening
Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the ...
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