Multi-hop Mobility Prediction

• Published in: Mobile networks and applications Vol. 21; no. 2; pp. 367 - 374
• Main Authors: Yu, Zhiyong, Yu, Zhiwen, Chen, Yuzhong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2016; Springer Nature B.V
• Subjects: Algorithms; Communications Engineering; Computer Communication Networks; Criteria; Electrical Engineering; Engineering; Global positioning systems; GPS; Habits; Human; IT in Business; Learning; Location based services; Markov analysis; Mobility; Networks; Similarity; State of the art; Studies; Trajectories; Wireless networks; GPS trajectories; Markov model; Crowd sensing; Multi-hop mobility prediction
• ISSN: 1383-469X; 1572-8153
• DOI: 10.1007/s11036-015-0668-2

With the occurrence of large-scale human trajectories, which imply spatial and temporal patterns, the subject of mobility prediction has been widely studied. A number of approaches are proposed to predict the next location of a user. In this paper, we expect to lengthen the temporal dimension of prediction results beyond one hop. To predict the future locations of a user at every time unit within a specified time, we propose a Markov-based multi-hop mobility prediction (Markov–MHMP) algorithm. It is a hybrid approach that considers multiple factors including personal habit, weekday similarity, and collective behavior. On a GPS dataset, our approach performs prediction better than baseline and state-of-the-art approaches under several evaluation criteria.