Internet of Things-Based Smart Traffic Light System for Hassle Free Movement of Emergency Vehicles

Internet of Things-Based Smart Traffic Light System for Hassle Free Movement of Emergency Vehicles

Muthurajkumar S., Danush Gupta V. K., Siddharth Gupta Vijjappu Parvatheeswara
Copyright: © 2023 |Pages: 19
DOI: 10.4018/978-1-6684-6697-1.ch022
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Abstract

Given the increasing number of vehicles on the road, the intensity of vehicular traffic in cities has tremendously increased. Because of the heavy traffic, there are often traffic delays on the roads, which can result in the loss of human life when emergency medical vehicles like ambulances and fire engines are stuck in the traffic jam. Traffic congestion is thought to be responsible for 30-35% of mortality in emergency situations, according to data compiled from various sources. In such conditions, it becomes essential that the traffic keeps flowing at a faster rate but in a smooth manner. The authors have devised a strategy to cut the amount of time an emergency vehicle spends at signal stops and the amount of manpower required at each traffic signal. The project's primary objective is to use IoT sensors to detect the arrival of emergency vehicles and reduce the time it takes for the vehicle to pass the traffic lights by favouring the lane in which the emergency vehicle is detected by turning traffic lights green to allow other vehicles in front to give way to the emergency vehicle.
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Background

Karthik B V et al. (2019) offered a system that employed a GPS module to send the ambulance's location to the cloud server through a Wi-Fi module that would be further sent to a smart traffic technology that dynamically modifies the traffic light sequence. According to their approach, there's a good probability the emergency vehicle was simply travelling parallel to the signal (while in the vicinity of the signal), with no need to actually cross the signal. The ability to disrupt the traffic signal's smooth operation caused a great deal of chaos.

Raman et al. (2020) have offered the SSD Mobilenet model, which uses an architecture based on convolutional neural network (CNN) to do object detection using real-time image processing. Furthermore, to eliminate probable false positives, an acoustic signal processing method was used to recognize the sirens of emergency vehicles. For early detection of emergency vehicles, the camera and sound sensors are located 400 meters distant from the traffic signals. But, their system has no option to prevent the emergency vehicle getting stuck in traffic after crossing the sensors under their proposed strategy.

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