Virtual Tollgate using Machine Learning

The traffic at toll gates is increasing day by day which leads to loss of time as well as fuel and pollution which is now a major concern. Even though there are smart payment gateways such as FASTAG the vehicle still needs to move in lower speeds to get detected by the readers. Our system is to implement a way smarter tollgate using machine learning algorithms. In these a tracking device is placed inside the corresponding vehicle and the path travelled by the vehicle for a day is retrieved as an image and pushed to the server through the internet connectivity provided in the device. In the server the received image is processed using the machine learning algorithms fed into it and allots the fare accordingly. The bill is generated on a monthly or weekly basis and sent to the user/owner of the vehicle. The utilization of mechanized cost assortment frameworks in numerous metropolitan urban areas would be an effective advance towards the congestion of the city roadways in substantial clog of traffic. As we as a whole know, transportation is the strength of our nation’s economy. There are different usage conventions in remote sensor organizations, for example, drain and segments, for example, RFID, NFC in this manner empowering decrease in activity expenses and rousing credit only exchanges. If there should arise an occurrence of manual cost assortment framework time utilization is much far more regrettable just as fuel consumption and most significant is the climate, the measure of air contamination that is made at the tollgate site is at elevated level, so our created framework will decrease time wastage and lessen air contamination as well as preserve fuel. The sole reason for this paper is to decrease the difficulties brought about by manual cost assortment frameworks and pass the subject’s vehicle through the cost obstruction in a matter of a couple of moments without stop.