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

Driverless automatically guided vehicles are becoming a new trend in transportation envisioned in a smart city environment. Automatically guided vehicles depend on environmental data for executing target-oriented navigation and movements. The traffic knowledge can be used by automatically guided vehicles by measuring their intensity and delivery date in the green light. Therefore, considering sensor data's significance in this guided vehicle environment, this article presents the Internet of Things (IoT) assisted automated guided vehicle (IoT-AGV) scheme. IoT-AGV scheme discusses the implementation of an autonomous vehicle traffic control program at crossings. The traffic knowledge operator is ready to retrieve details from the sensor utilizing quick communication through a mobile network to measure optimum travel time and transmit the information collected to automated cars. In an automated guided vehicle (AGV) scheme, the automotive-into-loop model is introduced to eliminate delays. Error indicator integrates various hurdle imbalance statistics calculated from the sensor based on the road slide. A deep neural network focuses on a minimal collection of the most critical features, eliminating the fitting problem. This aids determine the error producing sensor data and alleviate them from fusion to enhance the target's accuracy. The experimental results show that the suggested system has been validated by the images from BIT vehicle datasets and enhances the accuracy ratio of 96.22% to measure optimum travel time and transmit the information collected to automated guided vehicles. [ABSTRACT FROM AUTHOR]