GIS and Neural Network Method for Potential Road Identification

Abstract

Global positioning system (GPS)-based vehicle tracking systems were used to track 20 vehicles involved in an 8-day field training exercise at Yakima Training Center, Washington. A 3-layer feed-forward artificial neural network (NN) with a backpropagation learning algorithm was developed to identify potential roads. The NN was trained using a subset of the GPS data that was supplemented with field observations that documented newly formed road segments resulting from concentrated vehicle traffic during the military training exercise. The NN was subsequently applied to the full vehicle movement data set to predict potential roads for the entire training exercise. Model predictions were validated using additional installation and site visit data. The first validation used the NN to identify the existing road network as represented in the Yakima Training Center GIS roads data layer. Next, the NN was used to predict emerging road networks that had not previously existed. The NN method accurately classified approximately 94% of the training data, 85% of the on-road movement data, and 78% of potential roads. The proposed NN method more accurately classified potential roads than the previously used multicriteria method, which was able to identify 10 out of 17 potential road segments across the entire training center.