Traffic Congestion and Emergency Vehicle Responsive Traffic Signal Control in Resource Constrained Environment

Abstract

With the increase in the number of vehicles on the road, traffic congestion has become a major problem in metropolitan areas. Generally, the traffic flow through a junction is controlled using static traffic lights which are unable to adapt to the real-time traffic condition at a junction and do not prioritize the movement of certain types of vehicles. Emergency vehicles (e.g. ambulance, fire, police, etc.) play a crucial role in all life-threatening situations, and ensuring their movement through a congested junction with minimal time delay is essential.In this paper, we propose an adaptive and efficient traffic signal control system for less-lane disciplined heterogeneous (mixed) traffic that can be easily integrated with the existing static traffic lights in a resource-constrained environment. A sound sensor-based emergency vehicle detection system is designed that accurately detects and classifies emergency vehicles by identifying their unique siren sound. The traffic camera data are processed in real-time to compute the PCU counts at every approach of a junction and to detect emergency vehicles that do not generate siren sounds. The experiment results show 100% accuracy in emergency vehicle detection, more than 95% accuracy in the emergency vehicle classification, and 65% accuracy in vehicle classification and PCU count. We also design a queuing theory-based cost function that considers the prevailing traffic condition and the presence of priority vehicle(s) at a junction. The cost function can be used to adapt the green phase of different approaches at a junction to improve the vehicle flow through the junction while minimizing the delay for the emergency vehicles.