IoT-based traffic prediction and traffic signal control system for smart city

Because of the population increasing so high, and traffic density remaining same, prediction has become a great challenge today. Creating higher degree communication in automobiles results time wastage, fuel environmental damage, even death caused by citizens being trapped middle traffic. Only few researchers work congestion control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based using OWENN algorithm signal system Intel 80,286 microprocessor for smart city. The consists ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized values, system. Initially, are collected from dataset. After that, traffic, weather, direction information extracted, these extracted features given as input to classifier, which classifies place more Suppose one optimizes values IBSO, finally, is controlled microprocessor. An extracting features, classification performed step. Hereabout, done weight Elman neural network (OWENN) that places have attains 98.23% accuracy than existing model also its achieved 96.69% F-score model. experimental show outperforms state-of-the-art methods.