Either a user-defined training data set or a default training data set is selected (step 1). The default training data set consists of several secondary roads from the Czech road network. The training data set is generalized (step 2) and six explanatory variables (attributes) are computed for each vertex:
See Andrášik and Bíl (2016) for details. Subsequently, a naïve Bayes classifier is constructed from the training data set (step 3). The kernel density estimation is applied to calculate univariate probability densities in “tangent” and “horizontal circle” groups.
Then a user input the entire data set (step 4). Generalization is processed and explanatory variables are computed for the entire data (step 5). Then, the naïve Bayes classifier is used for identification of horizontal curves and tangents (step 6). The Least squares method (step 7) and heuristics (step 8) are applied to estimate horizontal curve radii and to identify composite horizontal curves. Finally, the output file containing individual road alignment geometry is created (step 9). Every record (road segment has the following attributes:
Furthermore, three new fields with curvature attributes are added to the original line data file (step 10):
Our approach for road geometry identification was published at first in the Journal of Geographical Systems:
Example of the segmented Czech road network by ROCA analysis can be viewed at web-map application: