Abstract
The increase in vehicles on the road causes an increase in traffic conditions, which induces difficulty in clearing the path for emergency vehicles such as ambulances, rescue fire engine and emergence vehicles. To avoid difficulty wait in traffic signal for the emergency situation, an IoT sensor smart-based system is developed that identifies the arrival of an ambulance to the hotspots through the signals received from the ambulance and maintains the signals accordingly. The regulation of the signals based on the location of the ambulance paves the way for the ambulance and rescue fire engine to urge its destination. This can be achieved via a device that incorporates a camera and sensor placed on the hotspots, as a Smart Network Traffic Signal (SNTS) detects and locates the vehicle and performs formulations to regulate traffic before the traffic signal. Machine learning algorithms are implicated to develop the model for the detection of the ambulance through the signals received. Convolutional Neural Network (CNN) and MobileNet algorithms employed yield 81 and 99% efficiency and are considered accurate for this system.
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Senthil, G.A., Prabha, R., Suganthi, S., Sridevi, S., Shanthi, N. (2023). An IoT-Enabled Smart Network Traffic Signal Assistant System for Emergency Vehicles Using Computer Vision. In: Raj, J.S., Perikos, I., Balas, V.E. (eds) Intelligent Sustainable Systems. ICoISS 2023. Lecture Notes in Networks and Systems, vol 665. Springer, Singapore. https://doi.org/10.1007/978-981-99-1726-6_37
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