Abstract
This paper provides an overview of work undertaken over the past two years to develop Artificial Neural Network (ANN) techniques to improve the accuracy and reliability of road selection during map-matching (MM) computation. MM positions provided by low-cost GPS receivers have great potential when integrated with hand-held or in-vehicle Geographical Information System (GIS) applications, especially those used for tracking and navigation, on path and road networks. The applied modular neural network (MNN) approach is using a suitable road shape indicator to incorporate different road shapes for local ANN training. MNN test results indicate good potential for the method to provide a significant improvement in MM and positional accuracy over traditional methods. Further results and conclusions of this on-going research will be published in due course.
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Winter, M., Taylor, G. (2006). A Modular Neural Network Approach to Improve Map-Matched GPS Positioning. In: Carswell, J.D., Tezuka, T. (eds) Web and Wireless Geographical Information Systems. W2GIS 2006. Lecture Notes in Computer Science, vol 4295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11935148_8
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DOI: https://doi.org/10.1007/11935148_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49466-9
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