{"title":"使用序列对齐的车队检测","authors":"Kai Li, Mark McKenney","doi":"10.1145/3357000.3366138","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate methods to detect convoys in trajectory data with locations sampled at irregular time intervals. In such cases, convoys that exist may not be detected in some algorithms. We explore three methods, one that involves adding interpolated points to trajectories, one that introduces flexibility to the temporal dimension, and one that uses sequence alignment. The algorithms are evaluated against a real-world data set.","PeriodicalId":153340,"journal":{"name":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Convoy Detection using Sequence Alignment\",\"authors\":\"Kai Li, Mark McKenney\",\"doi\":\"10.1145/3357000.3366138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we investigate methods to detect convoys in trajectory data with locations sampled at irregular time intervals. In such cases, convoys that exist may not be detected in some algorithms. We explore three methods, one that involves adding interpolated points to trajectories, one that introduces flexibility to the temporal dimension, and one that uses sequence alignment. The algorithms are evaluated against a real-world data set.\",\"PeriodicalId\":153340,\"journal\":{\"name\":\"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science\",\"volume\":\"109 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3357000.3366138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3357000.3366138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we investigate methods to detect convoys in trajectory data with locations sampled at irregular time intervals. In such cases, convoys that exist may not be detected in some algorithms. We explore three methods, one that involves adding interpolated points to trajectories, one that introduces flexibility to the temporal dimension, and one that uses sequence alignment. The algorithms are evaluated against a real-world data set.