IoT-based traffic prediction and traffic signal control system for smart city
subject: Improved beetle swarm optimization (IBSO); Intel 80,286 microprocessor; Internet of Things (IoT); Optimized weight elman neural network (OWENN); Smart city
description: 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. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
creator: Neelakandan S.; Berlin M.A.; Tripathi S.; Devi V.B.; Bhardwaj I.; Arulkumar N.
source: Soft Computing, Vol-25, No. 18, pp. 12241-12248.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2021-01-01
rights: Restricted Access
relation: ISSN: 14327643
format: Online
language: English
type: Article
coverage: Neelakandan S., Department of IT, Jeppiaar Institute of Technology, Chennai, India; Berlin M.A., Department of CSE, R.M.D Engineering College, Chennai, India; Tripathi S., Department of Computer Science & Engineering, Birla Institute of Applied Sciences, Bhimtal, India; Devi V.B., Department of Information Technology, Sri Sai Ram Institute of Technology, Chennai, India; Bhardwaj I., Galgotias University, Greater Noida, India; Arulkumar N., Department of CS, CHRIST (Deemed To Be University), Banglore, India