Most cities — from dense megacities to small towns — know the same frustrating story: a small crack is missed, it grows, and the eventual repair is costly, disruptive, and politically visible. Traditional maintenance systems are frequently reactive, relying on scheduled inspections or visible complaints. This leads to inefficient spending, avoidable traffic disruption, and shortened pavement life. In my recent paper, Enhancing Asphalt Management: Machine Learning for Predictive Maintenance and Sustainability in Urban Areas (International Journal of Pavement Research and Technology, 2025), I explored whether machine learning can offer a practical, deployable way to predict pavement failures before they escalate — and whether that approach can be made useful for cities regardless of size or budget. The result is a pragmatic, versatile framework designed for real municipal conditions.

The problem — simple and universal

Urban road networks face three persistent problems: Pavements age and experience traffic loads beyond their original design, creating many localized failure risks. Municipal budgets are finite, and emergency repairs consume disproportionate resources. Municipal data are often fragmented and inconsistent, making many advanced technical solutions difficult to apply. A workable solution must therefore be accurate enough to reduce emergencies, affordable to operate, and robust to the patchy data realities that many administrations face.

A realistic, no-frills approach

Rather than testing models on idealized datasets, I trained and evaluated approaches on a comprehensive municipal dataset spanning multiple years that intentionally preserved typical imperfections (gaps, inconsistent formats, and heterogeneous entries). The goal was not to chase the last decimal point of academic accuracy but to build models that municipal staff could realistically adopt. Two complementary model classes formed the core of the work: A modern tree-based ensemble method that excels at handling noisy, imbalanced data and can extract complex feature interactions. A simple, transparent logistic baseline that is fast to run and easy for non-technical stakeholders to interpret. This tiered design gives cities a clear choice: use higher-capacity models where data and compute resources justify them, and adopt the simpler, explainable alternative where resources or technical staff are limited.

Why this matters for megacities and small towns alike

A central strength of the study is its deliberate focus on versatility. The framework was built so that both large metropolitan agencies and smaller municipal teams can benefit: Scalable sophistication. Large cities can deploy the higher-capacity model for critical corridors and system-wide prioritization, while smaller towns can use the simpler model to get immediate, trustworthy guidance without heavy infrastructure investment. Tolerance for messy data. Training on real municipal records increased model robustness to missing or inconsistent inputs, lowering the barrier to early adoption. Practical integration. The approach works with common municipal data types — traffic counts, weather logs, and maintenance histories — and can be phased in using cloud or hybrid options if local compute is limited. Budget sensitivity. The study balances predictive performance with interpretability and operational cost, making it realistic for institutions that must justify investments to elected officials and the public. Because of these design choices, this work is not limited to well-funded, tech-savvy cities; it is intentionally inclusive, offering a path for a wide range of local governments to move from reactive repair toward proactive maintenance.

Impact — practical benefits, not just numbers

When municipal teams use predictive insights, they can prioritize inspections and repairs more effectively, avoid disruptive emergency interventions, and extend pavement life through timely, targeted actions. These operational improvements lead to safer roads, reduced public inconvenience, and more efficient use of taxpayer funds. Importantly, the framework is intended to complement — not replace — engineering judgment: models surface priority areas, and human teams make context-sensitive decisions.

Implementation pointers (practical checklist)

Inventory & digitize: Consolidate road condition records, maintenance logs, and basic traffic/weather data. Pilot: Start with a high-priority corridor to demonstrate value and tune workflows. Adopt tiers: Use higher-capacity models for high-risk zones and simpler models for neighborhood-level planning. Train & communicate: Provide short trainings and simple dashboards so engineers and decision-makers can trust and act on outputs. Measure outcomes: Track repair frequency, budget impacts, and disruption metrics to evaluate benefits.

Limitations and next steps

The current evaluation focused on temperate urban environments; adapting models for arid, tropical, or mountainous regions will require additional validation. Future extensions include integrating drone imagery, low-cost IoT sensors, satellite data, and crowdsourced reports to increase temporal and spatial resolution, plus transfer-learning methods to reduce local labeling needs. Equally important are investments in data governance and stakeholder engagement to ensure long-term trust and sustainability.

Paving the Way Forward

Predictive pavement management is not a magic cure, but it is a pragmatic and scalable tool that cities of any size can use. By designing for messy, realistic municipal data and offering a tiered, cost-aware pathway to deployment, this work shows how machine learning can help cities move from reactive maintenance to proactive stewardship — saving money, reducing disruption, and making streets safer and more sustainable for everyone.

Reference

Yasin Asadi (2025). Enhancing Asphalt Management: Machine Learning for Predictive Maintenance and Sustainability in Urban Areas. International Journal of Pavement Research and Technology.

DOI: https://doi.org/10.1007/s42947-025-00623-3