Vehicular Mobility Prediction by Bayesian Networks

Abstract

In mobile and ubiquitous computing the location of devices
is often important both for the behavior of the applications
and for communication and other middleware functionality.
Mobility prediction enables proactively dealing
with changes in location dependent functionality. In this
project Bayesian networks’ ability to reason on the basis of
incomplete or inaccurate information is powering mobility
prediction based on a map of the street grid and the current
location and direction of the vehicle. We found that it
is feasible to divide information of a map into smaller parts
and generate a Bayesian network for each of these in order
to make mobility prediction based on localized information.
This makes the information stored in the Bayesian networks
more manageable in size, which is important for resource
constrained devices. Common sense knowledge of how vehicle
moves is feeded into the networks and enables them
to make a good prediction even when no information of the
vehicles mobility history is used. Experiments on real world
data show that in an area statically divided into hexagonal
cells of 200m in diameter, we get 80.54% accuracy when
using localized Bayesian networks to predict which cell a
vehicle enters next.