Predicting future locations with hidden Markov models

Wesley Mathew · Ruben Raposo · Bruno Martins

384 citations0/0 files storedACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

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Human Mobility and Location-Based Analysis Data Management and Algorithms Traffic Prediction and Management Techniques

Abstract

The analysis of human location histories is currently getting an increasing attention, due to the widespread usage of geopositioning technologies such as the GPS, and also of online location-based services that allow users to share this information. Tasks such as the prediction of human movement can be addressed through the usage of these data, in turn offering support for more advanced applications, such as adaptive mobile services with proactive context-based functions. This paper presents an hybrid method for predicting human mobility on the basis of Hidden Markov Models (HMMs). The proposed approach clusters location histories according to their characteristics, and latter trains an HMM for each cluster. The usage of HMMs allows us to account with location characteristics as unobservable parameters, and also to account with the effects of each individual's previous actions. We report on a series of experiments with a real-world location history dataset from the GeoLife project, showing that a prediction accuracy of 13.85% can be achieved when considering regions of roughly 1280 squared meters.

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References

2008Understanding individual human mobility patterns 2008Understanding mobility based on GPS data 2012Next place prediction using mobility Markov chains 2012The Mobile Data Challenge: Big Data for Mobile Computing Research

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2020Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!2016GMove 2016Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest 2018Individual mobility prediction using transit smart card data 2019Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence 2017A hybrid Markov-based model for human mobility prediction 2015Exploiting machine learning techniques for location recognition and prediction with smartphone logs 2020Context-aware Deep Model for Joint Mobility and Time Prediction

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