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Title	Parameter Tuned Deep Learning Based Traffic Critical Prediction Model On Remote Sensing Imaging
ID_Doc	41273
Authors	Ahmed S.H.; Al-Zebari A.; Zebari R.R.; Zeebaree S.R.M.
Year	2023
Published	Computers, Materials and Continua, 75, 2
DOI	http://dx.doi.org/10.32604/cmc.2023.037464
Abstract	Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder (CNN-AE) model for productive and accurate traffic flow. Next, the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization (BADSO) algorithm. The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%. © 2023 Tech Science Press. All rights reserved.
Author Keywords	deep learning; intelligent transportation systems; Remote sensing images; smart cities; traffic prediction

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