Vehicular Mobility Prediction by Bayesian Networks

DAIMI Report Series

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Title Vehicular Mobility Prediction by Bayesian Networks
Creator Schougaard, Kari
Description In mobile and ubiquitous computing the location of devicesis often important both for the behavior of the applicationsand for communication and other middleware functionality.Mobility prediction enables proactively dealingwith changes in location dependent functionality. In thisproject Bayesian networks’ ability to reason on the basis ofincomplete or inaccurate information is powering mobilityprediction based on a map of the street grid and the currentlocation and direction of the vehicle. We found that itis feasible to divide information of a map into smaller partsand generate a Bayesian network for each of these in orderto make mobility prediction based on localized information.This makes the information stored in the Bayesian networksmore manageable in size, which is important for resourceconstrained devices. Common sense knowledge of how vehiclemoves is feeded into the networks and enables themto make a good prediction even when no information of thevehicles mobility history is used. Experiments on real worlddata show that in an area statically divided into hexagonalcells of 200m in diameter, we get 80.54% accuracy whenusing localized Bayesian networks to predict which cell avehicle enters next.
Publisher Aarhus University
Date 2007-01-01
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source DAIMI Report Series; No 582 (2007): PB-582 Vehicular Mobility Prediction by Bayesian Networks
DAIMI Report Series; No 582 (2007): PB-582 Vehicular Mobility Prediction by Bayesian Networks
Language eng

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