MyWay: Location prediction via mobility profiling

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• 作者：R. Trasarti^a ; ^{trasarti@isti.cnr.it} ; R. Guidotti^b ; ^a ; ^{guidotti@di.unipi.it} ; A. Monreale^b ; ^a ; ^{annam@di.unipi.it} ; F. Giannotti^a ; ^{giannotti@isti.cnr.it}
• 关键词：Trajectory prediction ; Spatio-temporal ; Data mining
• 刊名：Information Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：64
• 期：Complete
• 页码：350-367
• 全文大小：2151 K
• 卷排序：64

文摘

Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.