Published Paper Details:

Emergency vehicles such as ambulances, fire trucks are delayed in traffic as they confront numerous challenges, and valuable human lives are lost as a result of bad traffic management. In this research, a model is proposed for predicting traffic heaviness on roadways utilising image processing techniques with an emergency vehicle recognition system, as well as a traffic signal management model based on information collected from real time traffic videos. The majority of traffic signal systems use a timing mechanism that switches the lights after a set amount of time. In some situations, this may be ineffective. The controller receives no feedback from the regulated variable (traffic volume) (traffic control system using lighting). For example, whether the road is crowded or not, the duration of the green light will always be the same. To solve these congestion problems, it is better to build a new control system; a smart and intelligent control system. The smart traffic light system senses the presence or absence of vehicles and reacts accordingly. The idea behind smart traffic systems is that drivers will not spend unnecessary time waiting for the traffic lights to change. In addition, on detecting emergency vehicles in a lane the traffic signal is turned to green immediately so that a free passage is provided to these vehicles. This would help the emergency vehicles to reach the destination on the time thereby saving time as well as loss of life.