<p>It is very unsafe for Emergency Vehicle (EV) to wait near the traffic intersection for green signal. It needs to reach its destination as fast as possible. Even though emergency vehicles have higher priority on the road, but still at the traffic signal there is lag to give enough priority to pass through the signal. Sometimes they need to wait for the signal to favor them in a congested traffic or they may have to take part in dangerous movement which may result in collision. The better way to reduce the waiting time of such vehicles is by making the traffic signal green for providing easy pass through. So that the safety of other vehicles are also taken care. In this paper, whenever the emergency vehicle is approaching preemption messages are provided to the traffic signal unit. So these signals interrupt the normal operation of the traffic light and make it green for the emergency vehicle. It is a type of Vehicle-to-Infrastructure (V2I) communication. The proposed system uses GPS method to get the location data and employs ZigBee for communicating efficiently with the intersection module. There is no requirement of Line-of-Sight employed in infrared based preemption methods. Also eliminate the false trigger occurring in sound (siren) based method due to noise and fading. Using this approach, it is possible to serve multiple emergency vehicles reaching the traffic intersection according to their distance from the intersection and accurately taking the decision to turn the signal green for the particular vehicle. There is no involvement of driver of the EV in anyway, it is fully autonomous and it can be used as a part of ITS. © 2016 IEEE.</p>
cited By 0; Conference of 2016 International Conference on Inventive Computation Technologies, ICICT 2016 ; Conference Date: 26 August 2016 Through 27 August 2016; Conference Code:125861
V. Kodire, Bhaskaran, S., and Vishwas, H. N., “GPS and ZigBee based traffic signal preemption”, in Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016, 2017, vol. 2.