Abstract
Earlier studies have shown that it is feasible to apply ANN to categorise user recorded trajectory data such that the travelled road types can be revealed. This approach can be used to automatically detect, classify and report new roads and other road related information to GIS map vendor based on a user travel behavior. However, the effect of trajectory variability caused by varying road traffic conditions for the proposed approach was not presented; this is addressed in this paper. The results show that the variability encapsulated within the dataset is important for this approach since it aids the categorisation of the road types. Overall the SDNN achieved categorisation result of about 71% for original dataset and 55% for the variability pruned dataset.
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Ekpenyong, F., Palmer-Brown, D. (2009). Applying Snap-Drift Neural Network to Trajectory Data to Identify Road Types: Assessing the Effect of Trajectory Variability. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_45
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DOI: https://doi.org/10.1007/978-3-642-03969-0_45
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