Paper Info
Title
A comparison of first- and second-order HMMs in the task of predicting the next locations of mobile individuals
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 addresses the problem of predicting human mobility on the basis of Hidden Markov Models (HMMs), an approach that 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 both regular and second-order HMMs. The experiments were made with a real-world location history dataset from the LifeMap project, and the results show that a high prediction accuracy, relative to the dificulty of the task, can be achieved when considering relatively small regions.
Year
DOI
Venue
2012
10.1145/2442810.2442823
MobiGIS
Keywords
Field
DocType
next location,mobile individual,hidden markov models,lifemap project,second-order hmms,location characteristic,human movement,widespread usage,human location history,high prediction accuracy,real-world location history dataset,human mobility
Data mining,Computer science,Artificial intelligence,Global Positioning System,Hidden Markov model,Location prediction,Unobservable,Machine learning
Conference
Citations 
PageRank 
References 
1
0.35
17
Authors
2
Name
Order
Citations
PageRank
Wesley Mathew11044.13
Bruno Martins244134.58