Traffic congestion is exacerbated by the proliferation of vehicles, ineffective
traffic management practices, and inadequate road infrastructure. Especially in cities,
people are struggling to have quick mobility. Improper traffic management leads to
longer transit times and contributes to fuel waste, air pollution, and increased accident
risk. Conventional traffic control systems set the service time for each lane based on its
nature. For example, fast lanes may get more service time than slow ones. It is a static
system. It uses fixed timing to service all the lanes irrespective of traffic density. It
results in increased traffic in a specific lane. In most countries, automated systems are
unavailable to service emergency vehicles (ambulances, firefighting vehicles, mobile
medical units, blood donation vehicles, and VIP vehicles) due to the enormous
implementation and maintenance costs. In this chapter, a low-cost edge device is
proposed for an intelligent traffic control system to address this issue. The proposed
system focuses on dynamic signaling and automatic green enablers for emergency
vehicles. Dynamic signaling controls the timing of the traffic control system based on
the lane's traffic density, which is measured by the You Only Look Once (YOLO V3)
algorithm. An automatic green light enabler enables the green light in advance for the
lane where the emergency vehicle's presence is 750 meters away from the traffic signal.
It deals with various types of emergency vehicles, such as ambulances, firefighting
vehicles, and mobile medical units. Computer vision and communication technologies
are utilized in designing a cost-effective and innovative traffic control system. It assists in efficiently managing traffic. It also services emergency vehicles at road
intersections. Decreasing the average waiting time for emergency vehicles at road
intersections contributes to society by saving lives.
Keywords: Computer vision, Emergency vehicles, Edge devices, IoT, LORA, Traffic.
