An Integrated sound-based traffic control system (green light) for ambulance prioritization at urban intersections

Abstract

Urban congestion poses a significant barrier to timely emergency response, particularly for ambulances navigating signalized intersections. This research introduces an integrated, sound-based traffic control system designed to autonomously detect ambulance sirens and grant real-time greenlight priority with minimal disruption to general traffic flow. The system employs low-cost IoT devices installed 10 meters upstream of each lane to continuously monitor ambient sounds. Using a machine learning classifier, a Support Vector Machine (SVM) trained on features such as MFCCs, spectral centroids, and energy metrics, the system achieved 98% accuracy in distinguishing ambulance sirens from urban noise. Once detected, a direction-identification algorithm selects the correct lane by comparing amplitude levels across sensors, while a dynamic timer, based on sound amplitude and speed estimation, calculates the optimal green-light duration. Field trials demonstrated subsecond decision latency and reliable lane identification in all 50 test cases. A convoy mode further supports uninterrupted passage for multiple emergency vehicles by dynamically extending the green phase upon detection of successive sirens. Compared to the conservative fixed 5-second buffer approach, the adaptive system using a 1.5-second dynamic margin reduced unnecessary green light hold time by more than half—cutting average cross-traffic disruption from 5% to just 2% per cycle—while maintaining safe and uninterrupted passage for ambulances across all 50 realworld trials These results affirm the feasibility of using audiobased IoT systems for real-time, intelligent traffic management, especially in resource-constrained urban settings.