Traditionally, long-term infrastructure-focused models have guided maintenance and planning strategies, relying on decades-long degradation models, historical wear-and-tear patterns, and periodic maintenance cycles. While these approaches have provided valuable strategic insights, they often struggle to adapt to the increasing complexity of modern transportation networks.
Rapid changes in traffic demand, extreme weather events driven by climate change, and advancements in construction materials are making long-cycle predictions less reliable. As a result, road operation and maintenance strategies based solely on long-term projections often lead to inefficiencies — either overestimating deterioration and prompting unnecessary repairs, or underestimating wear, leading to costly, large-scale interventions with significant environmental and economic consequences.
Today, advancements in data analytics, IoT sensors, and AI are enabling a shift toward short-cycle forecasting, allowing for near-instantaneous performance assessments. Smart infrastructure now collects real-time data on road conditions, vehicle loads, and environmental impacts, making it possible to predict wear and necessary interventions with greater precision. For example, by integrating high-frequency pavement distress detection, maintenance teams can monitor road deterioration at a granular level, identifying minor defects before they escalate into severe structural damage. This proactive approach not only extends the lifespan of road networks but also reduces material waste, minimises carbon-intensive reconstruction activities, and optimises resource allocation — aligning with the broader goal of decarbonised, adaptable, and climate-resilient transport.
