Parametric distance functions vs. nonparametric neural networks for estimating road travel distances

Measuring and storing actual road travel distances between the points of a region is often not feasible and it is a common practice to estimate them. The usual approach is to use distance estimators which are parameterized functions of the coordinates of the points. We propose to use nonparametric approaches using neural networks for estimating actual distances. We consider multi-layer perceptrons trained with the back-propagation rule and regression neural networks implementing nonparametric regression using Gaussian kernels. We also consider training multiple estimators and combining them using voting and stacking. On a real-world study using cities drawn from Turkey, we found out that these nonparametric approaches are more accurate than the parametric distance functions. Estimating actual distances has many applications in location and distribution theory.