Research towards an innovative solution to the
problem of automated updating of road network
databases is presented. It moves away from existing
methods where vendors of road network databases
either go through the time consuming and
logistically challenging process of driving along
roads to register changes or use update methods that
rely on remote sensing images. The solution
presented here would allow users of road network
dependent applications (e.g. in-car navigation
system or NavSat) to passively collect characteristics
of any “unknown route” (departure from the known
roads in the database) on behalf of the provider.
These data would be processed either by an onboard
neural network or transferred back to the
NavSat provider and input to a neural net (ANN)
along with similar track data provided by other
service users, to decide whether or not to
automatically update (add) the “unknown road” to
the road database. This would be performed ‘on
probation’, allowing subsequent users to see the
road on their system and use it if need be. At a later
stage, when sufficient information on road geometry
and other characteristics has accumulated in order
to have confidence in the classification, the
probationary flag would be lifted and the new road
permanently added to the road network database. To
investigate this novel approach, GPS-based
trajectory data collected in London are analysed
using a Snap-Drift Neural Network (SDNN) and
categorised into different road class segments. The
performance of the SDNN and the key variables
required are presented
