Paper Info
Title
A hybrid Markov-based model for human mobility prediction.
Abstract
Human mobility behavior is far from random, and its indicators follow non-Gaussian distributions. Predicting human mobility has the potential to enhance location-based services, intelligent transportation systems, urban computing, and so forth. In this paper, we focus on improving the prediction accuracy of non-Gaussian mobility data by constructing a hybrid Markov-based model, which takes the non-Gaussian and spatio-temporal characteristics of real human mobility data into account. More specifically, we (1) estimate the order of the Markov chain predictor by adapting it to the length of frequent individual mobility patterns, instead of using a fixed order, (2) consider the time distribution of mobility patterns occurrences when calculating the transition probability for the next location, and (3) employ the prediction results of users with similar trajectories if the recent context has not been previously seen. We have conducted extensive experiments on real human trajectories collected during 21 days from 3474 individuals in an urban Long Term Evolution (LTE) network, and the results demonstrate that the proposed model for non-Gaussian mobility data can help predicting people’s future movements with more than 56% accuracy.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.05.101
Neurocomputing
Keywords
Field
DocType
Non-Gaussian mobility data,Hybrid Markov-based model,Human mobility,Mobility prediction,Spatio-temporal regularity
Individual mobility,Time distribution,Markov chain,Mobility model,Urban computing,Mobility prediction,Artificial intelligence,Intelligent transportation system,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
278
0925-2312
4
PageRank 
References 
Authors
0.42
35
6
Name
Order
Citations
PageRank
Yuanyuan Qiao110110.49
Zhongwei Si2437.79
Yanting Zhang363.16
Fehmi Ben Abdesslem418516.30
Xinyu Zhang5422.56
Jie Yang69411.97