Traffic Congestion Control Synchronizing and Rerouting Using LoRa

Abstract

Recent growth of Automobile users in big cities leads to traffic congestion. Traffic congestion leads to more waiting time for the vehicle users to reach destination. To avoid this road congestion, Cognitive Radio Networks (CRN) with proper allocation of spectrum, Bandwidth helps to divert the traffic at ease for the GPS enabled vehicle by applying Deep learning techniques. The proposed work introduces synchronization of two traffic signals using the Long Range (LoRa) module and concept of time division algorithm, that gives information about traffic by rerouting the vehicle to reach their destination with the shortest duration. Multi-level Traffic Monitoring Control (TMC) has the facility of sensing the information from the vehicle through transceivers (has the ability of gathering information and capturing the image of the road) receives the data from the vehicle and communicates to the server. The server processes captured image and communicates to the TMC. The TMC alerts vehicle users to divert their path by studying the multi-level TMC. The proposed concept helps vehicle users to take alternate direction by avoiding the congested traffic during peak hours. GPS enabled vehicle communicates the source and destination with live traffic to TMC, in turn receives the information with traffic free shortest route to reach destination.