Indigenous design of a Traffic Light Control system responsive to the local traffic dynamics and priority vehicles

Automation of traffic light control (TLC) systems is the major focus area compliant with Industry 4.0. The majority of the adaptive TLC systems are designed for homogeneous traffic of four-wheeler vehicles and are responsive to the prevailing traffic conditions at a junction. Further, they do not serve priority vehicles such as public transport (buses) or emergency vehicles (ambulance, fire trucks, etc.). The traffic dynamics considered in this work are less-lane-disciplined, heterogeneous traffic (two-three-four-wheeler vehicles), as found in the majority of developing countries. This work proposes a novel adaptive TLC system for heterogeneous traffic scenarios which is responsive to the prevailing traffic conditions at a junction, and also serves priority vehicles at a preference than the regular traffic. The proposed system supports multiple levels of priority among the priority vehicles, and efficiently schedules traffic lights at a junction to cause minimal interruption to the other regular traffic. For heterogeneous traffic monitoring, we use the Passenger Car Unit (PCU) count of the vehicles, which are computed using computer vision-based object detection models. The performance of deep learning-based object detection algorithms such as YOLO, RCNN, is analyzed experimentally for the computation requirements, and accuracy in computing PCU count. Simulations are carried out to analyze the effect of varying error rates in PCU count, and congestion level on the performance of the proposed adaptive TLC. The effect of the proposed adaptive TLC on the average travel and waiting times, of the priority vehicles and the other regular traffic is assessed. The simulation results suggest that the proposed algorithm can tolerate 20% error in the PCU count without degrading the performance. Additionally, this work demonstrates that the traffic information with the required accuracy can be processed in real-time using the available micro-controllers (e.g., Raspberry Pi or Jetson Nano). •A local adaptive TLC system (as per Industry 5.0) is proposed handling priority vehicles.•Supports multiple priority levels and efficiently schedules traffic lights at a junction.•Analyzed performance of deep learning-based object detection algorithms for PCU count.•Simulation-based performance evaluation is done capturing various traffic dynamics.•Includes additional class of vehicles (auto-rickshaws) in vehicle detection model.