An exploratory study of GPS trajectory data using Snap-Drift Neural Network

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

Updating of Road Network Databases: Spatio-Temporal Trajectory Grouping Using Snap-Drift Neural Network

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

Updating Road Network Databases: Road Segment Grouping Using Snap-Drift Neural Network.

At present a number of methods are being used to update road network databases including ground survey, driving along roads with GPS and analysing satellite images to register changes. Previous research has aimed at addressing three update functions: road extraction, change detection and change representation (Zhang, 2004). Different types of image processing algorithms have been developed for each purpose. While image-based road updating approaches have had success, their accuracy is directly tied to the quality of the data (Klang, 1998) and object model used for road extractions (Gerke et al., 2004). An alternative approach being investigated here is where service users of in-vehicle navigation systems might passively collect characteristics of any “unknown road” (roads not in the database) on behalf of the data provider

Automated updating of road network databases: road segment grouping using snap-drift neural network

Presented in this paper is a major step towards an innovative solution of GIS road network databases updating which moves away from existing traditional methods where vendors of road network databases go through the time consuming and logistically challenging process of driving along roads to register changes or GIS road network update methods that are exclusively tied to remote sensing images. Our proposed road database update solution would allow users of GIS road network dependent applications (e.g. in-car navigation system) to passively collect characteristics of any “unknown route” (roads not in the database) on behalf of the provider. These data are transferred back to the provider and inputted into an artificial neural net (ANN) which decides, along with similar track data provided by other service users, whether to automatically update (add) the “unknown road” to the road database on probation allowing subsequent users to see the road on their system and use it if need be. At a later stage when there is enough certainty on road geometry and other characteristics the probationary flag could be lifted and permanently added to the road network database. Towards this novel approach we mimicked two journey scenarios covering two test sites and aimed to group the road segments from the journey into their respective road types using the snap-drift neural network (SDNN). The performance of the SDNN is presented and its potential in the proposed solution is investigated

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