An Investigation Into Automatic Road Network Update Using Trajectory Data and Performance- Guided Neural Network
PhD Thesis
Ekpenyong, F. U. 2010. An Investigation Into Automatic Road Network Update Using Trajectory Data and Performance- Guided Neural Network. PhD Thesis University of East London School of Architecture, Computing and Engineering
Authors | Ekpenyong, F. U. |
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Type | PhD Thesis |
Abstract | This research aims to categorise road network recorded trajectory data using Artificial Neural Network (ANN) such that the travelled road class can be revealed. This would inform on the feasibility of implementing an automated road update system that would rely on user recorded trajectory data to automate the discovery, classification, and update of candidate road network segments to existing End-users of digital GIS road network database are increasingly the major source of road change error reports. At present, vendors of digital road network database only provide web forms for user to report road errors. To investigate these errors they travel such roads and analyse satellite images to register changes. However, the major limitations to this method are that it is time consuming and An alternative approach investigated in this thesis uses the trajectory of moving vehicles to automate the detection of new roads and thus update a road network database. GPS recorded trajectory data were collected during field tests from a range of road types. The trajectory data are an abstraction of the road segments travelled and this study assumes for the sake of experimentation that these road The results suggests that from the ANNs investigated, the unsupervised Snap-Drift Neural Network (SDNN) and the supervised Snap-Drift Adaptive Function Neural Network (SADFUNN) have the potential to support vehicle trajectory similarity grouping (classification) that can inform whether the road feature travelled is a new road feature that needs to be added to existing road database. The Probabilistic Neural Network (PNN) and Radial Basis Function (RBF) neural network also offered good classification performance. |
Keywords | Snap-Drift Neural Network; Artificial Neural Network; Geographic information system |
Year | 2010 |
Publisher | University of East London |
Publication dates | |
2010 | |
Publication process dates | |
Deposited | 12 Sep 2013 |
Additional information | This thesis supplied via ROAR to UEL-registered users is protected by copyright and other intellectual property rights, and duplication of any part of the material is not permitted, except for your personal use for the purposes of non-commercial research and private study in electronic or print form. You must obtain permission from the copyright-holder for any other use. Electronic or print copies may not be offered, for sale or otherwise, to anyone. No quotation from the thesis may be published without proper acknowledgement. |
Publisher's version | File Access Level Registered users only |
https://repository.uel.ac.uk/item/862w7
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