Ekpenyong, Frank and Palmer-Brown, Dominic and Brimicombe, Allan J. (2008) ‘An exploratory study of GPS trajectory data using Snap-Drift Neural Network’, Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 3rd Annual Conference. University of East London, pp. 22-30.
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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. For this approach we hypothesize that users of road network dependent applications (e.g. in-car navigation system or NavSat) could passively record drive trajectories with the on-board GPS, which would inform digital road network data providers if the user was on a road that departs from the known roads in the database. Then such drive characteristics would be collected using the on-board GPS on behalf of the provider. These data would be processed either by an on-board artificial neural network (ANN) or transferred back to the NavSat provider and input to an 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. As part of this work, in this paper we carry out an exploratory study on the trajectory information recorded with GPS. Trajectory data collected in London are analysed using a Snap-Drift Neural Network (SDNN) which categorises them into their strongest natural groupings, by combining clustering with feature detection in a single ANN. We investigate how the SDNN groups spatio-temporal variations associated with road traffic conditions. These variations are present in the recorded GPS trajectory data. For our approach which relies on users to passively record drive trajectory which are then processed as roads or not roads (Ekpenyong et al., 2007a), it is important to investigate how these variations affects the recorded GPS which influences the grouping by the SDNN. For our approach a question like – how would SDNN groups GPS recorded on a road segment in the morning (supposedly heavy traffic) to that recorded in the day (less traffic)? This issue is investigated in this paper.
|Divisions:||Schools > Architecture Computing and Engineering, School of|
|Additional Information:||Citation: Ekpenyong, F., Palmer-Brown, D., Brimicombe, A. (2008) ‘An exploratory study of GPS trajectory data using Snap-Drift Neural Network’ Proceedings of Advances in Computing and Technology, (AC&T) The School of Computing and Technology 3rd Annual Conference, University of East London, pp.22-30.|
|Date Deposited:||23 Jul 2010 09:13|
|Item Type:||Conference or Event Item (Paper)|
|Creators:||Ekpenyong, Frank and Palmer-Brown, Dominic and Brimicombe, Allan J.|
|Last Modified:||27 Sep 2012 11:59|
|Depositing User:||Stephen Grace|