Predicting Road Quality and Accessibility in Asia and the Pacific
Roads sit at the heart of economic and social life. They connect workers to jobs farmers to markets and communities to schools and healthcare. When roads fail those connections weaken and inequality deepens. Across Asia and the Pacific this reality plays out every day as governments struggle to maintain vast networks with limited budgets patchy data and growing climate pressures. Understanding road quality and accessibility is therefore not a technical exercise alone but a development imperative.
A recent Asian Development Bank technical report explores how conventional engineering surveys and emerging digital technologies can work together to improve how road quality and accessibility are measured. The study focuses on closing persistent data gaps that undermine infrastructure planning financing and long term maintenance strategies. In particular it examines how machine learning satellite imagery and smartphone based sensing can complement established methods to support Sustainable Development Goal Indicator 9.1.1 the Rural Accessibility Index.
Why Road Quality and Access Matter
The link between transport infrastructure and development is well established. Decades of research show that public investment in roads boosts productivity supports private sector growth and reduces poverty especially in rural areas. In developing economies rural road investment has consistently delivered stronger poverty reduction outcomes than many other infrastructure types.
The Asian Transport Observatory estimates that transport infrastructure investment needs in Asia and the Pacific will rise from around 750 billion dollars per year before 2020 to roughly 2.7 trillion dollars annually between 2020 and 2035. More than half of that requirement is tied to roads including construction maintenance and climate proofing. Without reliable data on road condition and access those investments risk being misdirected or delayed.
The Data Gap Holding Back Better Roads
Despite their importance road networks are often managed with incomplete or outdated information. Traditional road condition surveys require trained staff specialist equipment and repeated site visits. In remote or fragile regions access constraints weather and security risks further complicate data collection. As a result many countries lack consistent national level datasets on road quality.
This gap has consequences. Maintenance is frequently deferred until roads reach advanced stages of deterioration even though early intervention can cost a fraction of full reconstruction. International experience shows that neglecting minor defects can triple lifecycle costs. Climate change adds further uncertainty as heavier rainfall temperature extremes and flooding accelerate pavement degradation.
Understanding the Rural Accessibility Index
One of the most important global indicators tied to road access is Sustainable Development Goal Indicator 9.1.1 the Rural Accessibility Index. The RAI measures the proportion of the rural population living within two kilometres of an all season road. It is designed to capture whether people can reliably reach essential services throughout the year.
Although the RAI is conceptually clear it remains classified as a Tier 2 indicator meaning that while the methodology exists many countries do not produce data regularly. Only a handful of economies in Asia and the Pacific have official RAI values reported in the United Nations SDG Global Database. The challenge lies not in the definition but in the difficulty of measuring road condition consistently at scale.
Conventional Methods of Assessing Road Condition
Traditional pavement condition assessment remains the backbone of road management systems. These approaches focus on both structural performance which relates to load bearing capacity and functional performance which reflects ride comfort and safety. Engineers rely on pavement condition surveys to determine how well a road is performing and when maintenance is required.
Among functional indicators pavement roughness is the most widely used. Roughness captures the unevenness of the road surface and directly influences vehicle operating costs fuel consumption travel speed and user comfort. The globally accepted standard is the International Roughness Index expressed in metres per kilometre. Lower IRI values indicate smoother roads while higher values signal deterioration.
Measuring Roughness from Precision to Pragmatism
Roughness measurement technologies vary widely in accuracy cost and complexity. At the high end Class 1 and Class 2 devices such as laser based inertial profilers provide highly precise longitudinal profiles suitable for calibration and project level analysis. These systems deliver excellent data but are expensive and logistically demanding.
At the network level many agencies rely on Class 3 response type systems that estimate roughness based on vehicle suspension movement. While less precise these methods offer a practical balance between coverage and cost. Visual inspections and subjective ratings still play a role where resources are limited but their inconsistency reduces their usefulness for strategic planning.
Limitations of Traditional Approaches
Conventional surveys remain essential but they struggle to meet the scale and frequency required across large and diverse regions. Many developing economies face chronic underfunding of maintenance programmes and limited technical capacity. Data collection campaigns may occur only every few years leaving decision makers blind to rapid deterioration caused by extreme weather or traffic growth.
These constraints directly affect the ability to define all season roads for RAI calculations. Without reliable roughness or condition data roads may be misclassified undermining the credibility of accessibility statistics and weakening the evidence base for investment decisions.
Machine Learning and Remote Sensing Step In
Innovative technologies are beginning to change this picture. Advances in satellite imagery computer vision and machine learning allow road quality to be inferred from remotely acquired data. High resolution imagery can detect surface characteristics while algorithms trained on ground truth data can estimate roughness and deterioration patterns.
Several studies cited in the ADB report demonstrate promising results. Transfer learning approaches have achieved high prediction accuracy even when models trained in one country are applied to another. Smartphone sensors including accelerometers and GPS data collected from public transport vehicles have been used to estimate road roughness at very low cost. These methods offer scalable alternatives particularly for regions where traditional surveys are impractical.
Computer Vision and Smartphone Based Monitoring
Computer vision techniques analyse images to identify cracks potholes and surface wear. When combined with geospatial data they allow condition estimates to be mapped across entire networks. Smartphone based systems go further by crowdsourcing vibration data from ordinary vehicles turning daily travel into a continuous monitoring exercise.
While these approaches cannot yet replace precision engineering surveys they can flag emerging problems guide maintenance prioritisation and support preliminary assessments. Importantly they generate data at a frequency that traditional methods cannot match enabling earlier intervention and more resilient asset management strategies.
Implications for Financing and Policy
Better data changes the conversation around infrastructure finance. Reliable road quality and accessibility indicators strengthen the case for preventive maintenance which consistently delivers higher economic returns than reactive reconstruction. They also help development banks and governments target investments where they deliver the greatest social impact.
For SDG monitoring improved RAI measurement supports more credible reporting and international comparison. It highlights regions where access gaps persist and helps align transport policy with broader goals around poverty reduction inclusion and climate resilience.
Building Integrated Road Management Systems
The future of road quality monitoring lies in integration. Traditional surveys provide accuracy and engineering rigour while innovative methods deliver scale timeliness and affordability. Together they form a layered evidence base that supports both strategic planning and operational decision making.
The ADB technical assistance programme illustrates how this integration can work in practice. By piloting machine learning models trained on conventionally collected data the programme aims to reduce monitoring costs while maintaining credibility. For developing member countries this approach offers a pathway to stronger asset management without unsustainable financial burdens.
A Smarter Path Forward
Roads will continue to underpin development across Asia and the Pacific but their value depends on how well they are maintained and how equitably they serve populations. Measuring road quality and accessibility is therefore not an abstract technical task but a foundation for smarter policy better investment decisions and more resilient communities. By combining engineering rigour with scalable digital insight governments and development partners can move beyond reactive maintenance and towards evidence led infrastructure stewardship.
The challenge now is institutional rather than technical. Tools exist and pilot programmes are proving their worth. Embedding these approaches into national road management systems will require capacity building sustained financing and clear policy alignment. If that transition is made successfully improved road quality monitoring will not only support more credible SDG reporting but also deliver tangible gains in mobility inclusion and long term economic resilience.
Download the Report
The full technical report Predicting Road Quality and Accessibility Indicators through Conventional and Innovative Methods is published by the Asian Development Bank. It provides detailed methodologies case studies and technical annexes supporting the analysis summarised in this article.
The report can be downloaded free of charge in PDF and ebook formats from the Asian Development Bank publications portal It is recommended reading for transport authorities infrastructure planners development banks and policymakers involved in road asset management and rural accessibility assessment.
