Wijesiri, PLokuliyana, SJayasena, SWijesiri, SJayakody, AAbeygunawardhana, P.K.W2026-11-032025-12-09P. Wijesiri, S. Lokuliyana, S. Jayasena, S. Wijesiri, A. Jayakody and P. K. W. Abeygunawardhana, "Intelligent Traffic Signal Optimization Using Sound-Based Ambulance Detection and Green-Light Extension," 2025 7th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2025, pp. 1-6, doi: 10.1109/ICAC69156.2025.11361421.979-833156222-9https://rda.sliit.lk/handle/123456789/5408Urban traffic congestion significantly hinders ambulance mobility, particularly at intersections where static signals restrict timely passage. This study presents a sound-based intelligent traffic signal optimization framework that integrates siren detection with adaptive green-light extension. The system utilizes IoT audio sensors with Mel-Frequency Cepstral Coefficient (MFCC) feature extraction and a Support Vector Machine (SVM) classifier, achieving 98% detection accuracy in distinguishing ambulance sirens from urban noise. Direction identification reached 96.3% accuracy, while distance estimation maintained an average error of 5.8 m. A novel dynamic green-light timer, incorporating speed, distance, and error-correction metrics, reduced unnecessary signal holding by over 50%, lowering cross-traffic disruption from 5% to 2%. Field tests demonstrated sub-second latency and effective convoy handling for multiple ambulances. These results establish the framework as a lightweight, scalable, and cost-effective solution for real-time ambulance prioritization in resource-constrained urban environments.enAmbulance PrioritizationGreen-Light ExtensionIntelligent Traffic ManagementSound-Based DetectionSVMConference Paperdoi: 10.1109/ICAC69156.2025.11361421