Distance between Road Networks: A Macroscopic Method for Road Network Datasets Comparison Using Traffic-weighted Geographic Distribution

TL;DR

This paper introduces a quantitative method for comparing road network datasets by incorporating traffic flow data and using Wasserstein distance to evaluate differences.

Contribution

It presents a novel macroscopic approach that considers traffic-weighted geographic distribution for dataset comparison, extending beyond purely topological evaluations.

Findings

The method effectively distinguishes differences between datasets based on traffic flow.

Case studies demonstrate the method's potential in dataset evaluation and selection.

Abstract

In transportation network analysis, various types of road network data can be used even when focusing on the same region. Since different road network datasets can make different performance in analyses, it is necessary to compare them and make appropriate selections in a qualitative manner. However, many of the existing methods for comparing road network datasets are limited to specific topological evaluations and do not consider transportation. This study proposes a method for quantitative comparison of different road network datasets with explicit consideration for traffic flows on them. The method first conducts a static traffic assignment with hypothetical demand for each dataset, and then compare the results using Wasserstein distance on two dimensional plane. Case study on different sources of road network datasets and their simplifications suggests the potential use of the proposed method in evaluating and selecting road network datasets.