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Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

Urban mobility is one of the critical challenges in modern cities that should be tackled carefully. The exponential growth of cars number badly impacts the transportation system (TS) on which most cities are living on. Traffic control is one of the most critical issues in TS, which depends on a set of cooperative traffic lights. Smart Traffic lights, which can receive and analyze traffic data, can solve traffic problems by efficiently predict the accurate waiting time for each traffic lane at the intersections. This can improve the traffic flow and accordingly promotes the transportation system performance. This paper introduces a Fog-Based Traffic Light Management Strategy (TLMS) based on Fuzzy Inference Engine. TLMS can accurately calculate the optimal waiting time for each traffic lane at the intersections to decrease the average waiting time for the stopped vehicles. TLMS applies Vehicle to infrastructure protocol (V2I) that allows vehicles to interact directly with the infrastructure of the road such as GPS sensors and the traffic light signals. At each traffic intersection, the number of waiting vehicles, their locations relative to TLMS, and their sizes are detected and sent to TLMS in real time. Then, based on fuzzy inference, TLMS can calculate the optimal waiting time for each lane, which optimizes the traffic flow at the intersection by minimizing the waiting time for the vehicles. The performance of the proposed TLMS has been compared and tested against recently proposed techniques via simulation. The results of the experiment showed that TLMS outperforms recent technologies as it minimizes the average waiting time of vehicles around the intersections and accordingly maximizes the performance of the traffic system. [ABSTRACT FROM AUTHOR]

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]