An Investigation Into Automatic Road Network Update Using Trajectory Data and Performance- Guided Neural Network

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
road network database.

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
logistically challenging to visit all locations of reported road error. Also the accuracy of road user road error report depends on the user's interpretation of the road network representation offered on the device in relation to the road in the real world, and the user's geographic knowledge and familiarity of the area. In the literature, different solutions have been proposed to deal with the key road update functions road change detection, representation and update. But most of these approaches are exclusively tied to remote sensing images. While these methods of road updating have been successfully used to extract roads from images, their accuracy is directly tied to the quality of the images and object model used for road extraction. Hence, existing solutions are image-specific and cannot be applied to other image type obtained from another sensor without significant adjustments of the parameters.

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
segments are not present in the GIS road coverage and seeks to group the GPS-based trajectory data using an ANN to reveal the presence and class of public thoroughfares. This will establish the extent to which drive characteristics naturally fall into road feature classes.

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.

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.