An efficient traffic acoustic energy harvester using optimized Helmholtz resonators for sustainable roadside power generation and smart monitoring

Current approaches to traffic noise management primarily focus on control strategies, yet traffic noise exhibits characteristics of continuous generation and widespread distribution. Therefore, it becomes highly meaningful to harvest and utilize acoustic energy while controlling traffic noise. Furthermore, analyzing the characteristics of energy generation and integrating artificial intelligence can enable monitoring of various road conditions. This paper presents an acoustic energy harvesting system based on a Helmholtz resonator incorporating a front reflector configuration for sustainable roadside power generation and intelligent traffic monitoring. We derived theoretical formulations for the resonant frequency characteristics of the front reflector-enhanced Helmholtz resonator and validated these predictions through comprehensive numerical simulations. The results demonstrate excellent agreement between theoretical predictions and simulation results across most geometric parameter changes. We characterized the frequency distribution of traffic noise and optimized the acoustic energy harvester design to match these spectral characteristics. The power generation performance was quantified and compared across different structural configurations, demonstrating the superior energy output capabilities of the proposed design. Experimental validation confirmed the system’s dual functionality in energy harvesting and noise mitigation under real-world traffic conditions. Subsequently, we implemented a Multi-Scale Convolutional Neural Network algorithm to classify vehicle speed ranges based on acoustic signatures, achieving an accuracy of 95.61% in distinguishing between different speed categories. This speed classification framework enables intelligent control of road monitoring equipment activation, allowing the system to operate only when speeding vehicles are detected while maintaining a low-power sleep mode during normal traffic conditions, thereby achieving significant energy conservation for intelligent transportation systems. • Front reflector-enhanced Helmholtz resonator achieves 26.39 × sound amplification. • MSCNN algorithm classifies vehicle speeds with 95.61% accuracy from acoustic data. • System enables energy-efficient traffic monitoring by selective sensor activation. • Acoustic energy harvester generates 1.96V while providing about 20 dB noise reduction.