Tracking Road Quality in Developing Asia Using Satellites, Sensors, and Smart Data

The Asian Development Bank and its partners show that poor data on road quality and rural access weakens development outcomes, even though good roads are critical for growth, inclusion, and poverty reduction. The report argues that combining traditional engineering surveys with new tools like satellite imagery, machine learning, and smartphone data can deliver cheaper, faster, and more reliable road monitoring to support better policy decisions.

The Asian Development Bank (ADB), working alongside the World Bank, the United Kingdom’s Transport Research Laboratory (TRL), the Research for Community Access Partnership (ReCAP), national statistical offices, road agencies in countries such as the Philippines and Thailand, and academic and technology partners, has produced a detailed study on how road quality and accessibility can be measured more effectively across Asia and the Pacific. The report starts from a simple but powerful idea: roads are more than physical assets. They connect people to jobs, schools, hospitals, and markets, and their quality directly influences economic growth, poverty reduction, and social inclusion. When roads are well maintained, communities thrive; when they deteriorate, development gains quickly unravel.

Why Measuring Road Access Still Falls Short

Despite the importance of roads, many countries struggle to track how accessible and reliable their road networks actually are. This problem is especially visible in the Rural Accessibility Index (RAI), the official indicator for Sustainable Development Goal 9.1.1, which measures how many rural people live within two kilometers of an all-season road. Although the concept is clear, collecting the necessary data is difficult. Traditional surveys are expensive, time-consuming, and hard to conduct in remote areas. As a result, many countries do not report RAI regularly, leaving large gaps in national and global statistics. The report shows that without better data, governments cannot accurately plan investments or monitor progress toward development goals.

How Engineers Traditionally Measure Road Quality

The study explains road quality in straightforward terms by separating it into structural performance and functional performance. Structural performance relates to whether a road can carry traffic loads over time, while functional performance affects how comfortable and safe the road feels to users. One key functional measure is pavement roughness, which captures how uneven a road surface is. This is commonly measured using the International Roughness Index (IRI), a global standard expressed in meters per kilometer. Higher IRI values mean rougher roads, slower speeds, higher fuel consumption, and more vehicle damage. The report explains that different types of roads tolerate different roughness levels, which is why rural roads and expressways have different maintenance thresholds.

New Technologies Offer Faster and Cheaper Options

Because traditional road surveys are costly and slow, the report explores innovative alternatives. Advances in satellite imagery, machine learning, and computer vision now make it possible to assess road conditions remotely. Studies reviewed in the report show that computer models trained on satellite images can classify roads into broad condition categories and even estimate roughness levels. While these methods are not yet precise enough to replace engineering surveys, they are very useful for screening large road networks and identifying priority areas, especially in hard-to-reach regions.

Smartphones also play an important role. Modern phones contain sensors that can detect vibrations caused by rough roads. When combined with GPS and suitable algorithms, smartphone data can provide low-cost indicators of road roughness. The report notes challenges such as differences between phone models, driving behavior, and data privacy concerns, but concludes that with proper safeguards, smartphones could greatly expand road monitoring coverage.

A Practical Path Forward for Governments

Rather than choosing between old and new methods, the report recommends combining them. Satellite imagery and machine learning can be used for wide-area screening, smartphone data can help estimate roughness, and traditional surveys can be reserved for locations where detailed analysis is essential. This layered approach reduces costs while improving data coverage and timeliness. However, the report emphasizes that technology alone is not enough. Governments also need stronger institutions, trained staff, clear data standards, and safeguards for privacy and data security.

In closing, the study makes a clear case: better road data leads to better decisions. By blending proven engineering practices with modern data technologies and investing in skills and institutions, countries in Asia and the Pacific can maintain their roads more efficiently, improve rural access, and ensure that infrastructure truly supports inclusive and sustainable development.