Adaptive Traffic Control With Emergency Vehicle Prioritization

Abstract

The rapid expansion of urban areas and the exponential growth of vehicular populations have created unprecedented challenges for modern transportation systems. Traditional traffic signal infrastructures built on fixed-time cycles and outdated algorithms fail to respond to dynamic and unpredictable traffic patterns, resulting in gridlocks, increased travel durations, and heightened fuel consumption. More critically, emergency vehicles such as ambulances, fire tenders, and police units face severe delays at intersections due to their inability to bypass congested lanes, adversely impacting emergency response time and overall survivability in time-critical scenarios. In response to these limitations, this project introduces a fully integrated, multi-modal intelligent traffic management system combining RFID-based vehicle authentication, high-accuracy acoustic siren detection, and computer-vision-driven lane density analysis using OpenCV. A robust double-verification mechanism ensures that emergency signals are validated only when both modalities RFID and siren are detected within a strict temporal window, thereby eliminating false positives. Furthermore, real-time lane density estimation enables adaptive traffic signal allocation based on congestion levels, improving intersection throughput and reducing average waiting times. The results demonstrate significant gains in system reliability, emergency response optimization, and congestion mitigation, positioning this hybrid architecture as a viable and scalable solution for emerging smart-city ecosystems.