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A hybrid Markov-based model for human mobility prediction
Beijing University of Posts and Telecommunications, China.
Beijing University of Posts and Telecommunications, China.
Beijing University of Posts and Telecommunications, China.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7866-143x
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2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 278, no SI, p. 99-109Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
2018. Vol. 278, no SI, p. 99-109
Keywords [en]
Non-Gaussian mobility dataHybrid Markov-based modelHuman mobilityMobility predictionSpatio-temporal regularity
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33735DOI: 10.1016/j.neucom.2017.05.101OAI: oai:DiVA.org:ri-33735DiVA, id: diva2:1197806
Available from: 2018-04-14 Created: 2018-04-14 Last updated: 2018-07-19Bibliographically approved

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Ben Abdesslem, Fehmi

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CiteExportLink to record
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