Design and feasibility study of a smart speed hump for selective urban speed management: risk-informed deployment via conditional generative modelling - Kaya, Ömer (Frontiers in Future Transportation, 2026)
Urban speed management in developing countries frequently relies on fixed physical speed humps. While effective for compliance, these devices can reduce comfort for compliant drivers, increase structural loads on heavy vehicles, and complicate winter maintenance operations. This study develops and evaluates a selective speed-management approach centred on an adaptive speed hump concept that remains flush for compliant drivers and actuates only when speeding is likely or detected. To support deployment decisions in data-scarce settings, a conditional generative (parametric) decision-support module is used to generate synthetic speed distributions from roadway and scenario attributes based on sparse observations. Segment-level speed-violation risk is estimated and combined with additional criteria to compute a Speed-Calming Suitability Index (SSI) for site prioritization. A low-cost laboratory prototype with real-time speed detection and a servo-driven movable surface demonstrates selective actuation at a single point. The modelling workflow produces actionable risk and SSI-based prioritization for targeted traffic calming, and the prototype demonstrates the feasibility of selective actuation. Together, these components support risk-informed selection of candidate locations and practical implementation of a selective traffic-calming mechanism. The results suggest that conditional generative modelling can support sustainable mobility by enabling risk-informed deployment of adaptive traffic-calming infrastructure under data scarcity. Here, “generative” denotes distributional speed sampling for risk inference; the implementation is a lightweight parametric conditional generator (mean plus dispersion) rather than GAN/VAE/diffusion-style architectures.