Amnir Hadachi, Oleg Batrashev, Artjom Lind, Georg Singer, E. Vainikko
{"title":"基于增强马尔可夫链算法的手机用户移动性预测","authors":"Amnir Hadachi, Oleg Batrashev, Artjom Lind, Georg Singer, E. Vainikko","doi":"10.1109/IVS.2014.6856442","DOIUrl":null,"url":null,"abstract":"This article presents a mobility prediction method for mobile phone users based on an enhanced Markov Chain algorithm. The mobile phone data has a highly dynamic nature and a sparcely sampled aspect; therefore, the prediction of user's mobility location poses a challenge. Our enhancement approach can be summarized as an embedded association of rules applied to a Markov chain algorithm. The proposed solution is encouraging for the next generation of mobile networks and it can be used to optimize the existing mobile network infrastructure, road traffic, tracking systems and localization. Validation of our system was carried out using real data collected from the field.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":"{\"title\":\"Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm\",\"authors\":\"Amnir Hadachi, Oleg Batrashev, Artjom Lind, Georg Singer, E. Vainikko\",\"doi\":\"10.1109/IVS.2014.6856442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a mobility prediction method for mobile phone users based on an enhanced Markov Chain algorithm. The mobile phone data has a highly dynamic nature and a sparcely sampled aspect; therefore, the prediction of user's mobility location poses a challenge. Our enhancement approach can be summarized as an embedded association of rules applied to a Markov chain algorithm. The proposed solution is encouraging for the next generation of mobile networks and it can be used to optimize the existing mobile network infrastructure, road traffic, tracking systems and localization. Validation of our system was carried out using real data collected from the field.\",\"PeriodicalId\":254500,\"journal\":{\"name\":\"2014 IEEE Intelligent Vehicles Symposium Proceedings\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"34\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Intelligent Vehicles Symposium Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2014.6856442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Intelligent Vehicles Symposium Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2014.6856442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm
This article presents a mobility prediction method for mobile phone users based on an enhanced Markov Chain algorithm. The mobile phone data has a highly dynamic nature and a sparcely sampled aspect; therefore, the prediction of user's mobility location poses a challenge. Our enhancement approach can be summarized as an embedded association of rules applied to a Markov chain algorithm. The proposed solution is encouraging for the next generation of mobile networks and it can be used to optimize the existing mobile network infrastructure, road traffic, tracking systems and localization. Validation of our system was carried out using real data collected from the field.