Global patterns of current and future road infrastructure

TLDR

Georeferenced road infrastructure data is essential for planning, assessments, and impact analyses, yet current global maps are outdated or spatially biased. The Global Roads Inventory Project compiled and harmonized nearly 60 geospatial datasets, then related country road length to area, population density, GDP, and OECD membership, producing a regression model (adjusted R² = 0.90) that links higher road densities to denser, wealthier nations. The resulting dataset covers 222 countries and 21 million km of roads—two to three times the length of existing datasets—and projections estimate an additional 3.0–4.7 million km by 2050, especially in developing nations and pristine regions, underscoring the need for accurate road data to mitigate impacts on ecosystems.

Abstract

Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.