Paper: Estimating Road Travel Distances

[Ethem Alpaydin]

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                       Parametric Distance Metrics vs.
                      Nonparametric Neural Networks for
                      Estimating Road Travel Distances

                 Ethem Alpaydin*, I. Kuban Altinel+, Necati Aras+

			{alpaydin,altinel,arasn}@boun.edu.tr

			* Dept of Computer Engineering
			+ Dept of Industrial Engineering
                            Bogazici University
                         TR-80815 Istanbul Turkey

The actual distance between two cities is the length of the shortest
road connecting them. Measuring and storing the actual distance between
any two points of a region is often not feasible and it is a common
practice to estimate it. The usual approach is to use theoretical
distance metrics 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 in a
hybrid architecture using voting and stacking. On a real-world study
using cities drawn from Turkey, we found that out that these approaches
improve performance considerably. Estimating actual distances has many
applications in location and distribution theory.