Path-Based Distance for Street Map Comparison

TL;DR

This paper introduces a novel path-based distance measure for comparing street maps that considers both the spatial embedding and structural properties of transportation networks, providing a more comprehensive similarity assessment.

Contribution

The authors propose a new polynomial-time approximable distance measure that combines spatial and structural properties for comparing planar geometric graphs like street maps.

Findings

Distance measure preserves structural and spatial properties.

Approximation of the distance can be computed in polynomial time.

The method effectively captures differences in street map similarity.

Abstract

Comparing two geometric graphs embedded in space is important in the field of transportation network analysis. Given street maps of the same city collected from different sources, researchers often need to know how and where they differ. However, the majority of current graph comparison algorithms are based on structural properties of graphs, such as their degree distribution or their local connectivity properties, and do not consider their spatial embedding. This ignores a key property of road networks since similarity of travel over two road networks is intimately tied to the specific spatial embedding. Likewise, many current street map comparison algorithms focus on the spatial embeddings only and do not take structural properties into account, which makes these algorithms insensitive to local connectivity properties and shortest path similarities. We propose a new path-based distance measure to compare two planar geometric graphs embedded in the plane. Our distance measure takes structural as well as spatial properties into account by imposing a distance measure between two road networks based on the Hausdorff distance between the two sets of travel paths they represent. We show that this distance can be approximated in polynomial time and that it preserves structural and spatial properties of the graphs.