Smart Traffic Prediction and Congestion Reduction in Smart Cities

Abstract

Rising incidents of traffic congestion among the increasing usage of vehicles have a high concern in an urban area. Smart Traffic prediction helps in reducing the traffic level in urban areas. Through Wi-Fi, Bluetooth, Zig Bee technologies, signals from the smart devices used in vehicles are received. Received signal stored as digital data and used for analyze the traffic pattern by vehicle count. From the input received signal, the traffic patterns are identified from the prediction process using a machine learning algorithm. People can easily view the result of the traffic level in pictorial or graphical form. It is the technology to support the monitoring and controlling system in the road traffic using sensors and cloud-based prediction algorithms. In this proposal, to minimize the traffic congestion in certain areas by diverting or redirecting the upcoming vehicles into the shortest path or alternate path based on prediction methods. Accuracy level of prediction methods are compared for better result. Traffic prediction and rerouting reduces the level of traffic flow and air pollution (gasoline emission) and give us traffic-free urban roadways.