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This solution uses smartphone data and AI to effortlessly and automatically assess road infrastructure conditions, including roughness and anomalies, supporting data-driven maintenance prioritization, transparent decision-making, and historical records for compliance purposes.
Road management bodies need to decide where, when, and why to spend a limited maintenance budget across an extensive network — and to justify those decisions on objective grounds. To meet this need, the road management body we worked with relied on sporadic field surveys and citizen complaints, since systematic, network-wide inspections demand specialized equipment and dedicated staff that the body could not sustain. This baseline was leaving three persistent gaps: early-stage deterioration went unnoticed until it became a costly repair; condition data aged quickly between surveys; and there was no consistent historical record to back decisions in audits, reporting, or funding requests. The result was maintenance that was largely reactive and prioritization that was difficult to defend with evidence.
The use case deployed SmartRoadsAI, a platform that turns standard smartphones already riding in municipal fleets into a passive sensing layer for continuous road monitoring — without dedicated inspection vehicles, additional staff time, or new hardware. Two complementary data streams — inertial signals that track overall pavement condition along each segment and flag discrete events such as potholes, and video that classifies defects in detail — let the road management body see early-stage degradation as it emerges, keep a current view of the network as vehicles drive their normal routes, and build a consistent historical record of how each segment evolves over time. Results are delivered through a web dashboard that can integrate with existing municipal workflows, so prioritization decisions can be backed with objective evidence. The solution was deployed in a real operational context with the road management body to test technical feasibility on local roads, calibrate accuracy against ground-truth checks, and validate the workflow end-to-end.
