Probabilistic intersection noise mapping for urban sustainability in Canada and Malaysia

Road traffic noise is a persistent urban environmental burden that can undermine health, livability and progress toward SDG 11. This study develops a probabilistic simulation framework for crossroad traffic-noise mapping by explicitly representing stochastic traffic volume and vehicle-type composition on each intersection approach. The framework provides an end-to-end workflow from probabilistic traffic characterisation to spatial prediction and noise-map generation. Field measurements were conducted at six urban intersections (three in Malaysia and three in Canada) to validate accuracy and reliability. Across the sampled receiver locations, the model generally produced low percentage errors and small mean absolute differences. The resulting maps illustrated intersection-scale hotspot patterns and the potential for scenario testing. Overall, the findings provide proof-of-concept evidence that probabilistic, intersection-aware noise mapping can support hotspot screening, mitigation planning and scenario evaluation aligned with SDG 11’s aim of creating more sustainable and livable cities.