{"title":"弹道数据的K-BestMatch重构与比较","authors":"M. Nanni, R. Trasarti","doi":"10.1109/ICDMW.2009.62","DOIUrl":null,"url":null,"abstract":"In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering.","PeriodicalId":351078,"journal":{"name":"2009 IEEE International Conference on Data Mining Workshops","volume":"76 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"K-BestMatch Reconstruction and Comparison of Trajectory Data\",\"authors\":\"M. Nanni, R. Trasarti\",\"doi\":\"10.1109/ICDMW.2009.62\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering.\",\"PeriodicalId\":351078,\"journal\":{\"name\":\"2009 IEEE International Conference on Data Mining Workshops\",\"volume\":\"76 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Data Mining Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2009.62\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Data Mining Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2009.62","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
K-BestMatch Reconstruction and Comparison of Trajectory Data
In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering.