Open Access iconOpen Access

ARTICLE

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Manar Ahmed Hamza1,*, Hadeel Alsolai2, Jaber S. Alzahrani3, Mohammad Alamgeer4,5, Mohamed Mahmoud Sayed6, Abu Sarwar Zamani1, Ishfaq Yaseen1, Abdelwahed Motwakel1

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
4 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Department of Computer Science and Bioinformatics, Singhania University, Pacheri Bari, District Jhnujhunu, Rajasthan, India
6 Department of Architectural Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computers, Materials & Continua 2022, 73(3), 6563-6577. https://doi.org/10.32604/cmc.2022.031541

Abstract

Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using min-max normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

Keywords


Cite This Article

APA Style
Hamza, M.A., Alsolai, H., Alzahrani, J.S., Alamgeer, M., Sayed, M.M. et al. (2022). Intelligent slime mould optimization with deep learning enabled traffic prediction in smart cities. Computers, Materials & Continua, 73(3), 6563-6577. https://doi.org/10.32604/cmc.2022.031541
Vancouver Style
Hamza MA, Alsolai H, Alzahrani JS, Alamgeer M, Sayed MM, Zamani AS, et al. Intelligent slime mould optimization with deep learning enabled traffic prediction in smart cities. Comput Mater Contin. 2022;73(3):6563-6577 https://doi.org/10.32604/cmc.2022.031541
IEEE Style
M.A. Hamza et al., "Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities," Comput. Mater. Contin., vol. 73, no. 3, pp. 6563-6577. 2022. https://doi.org/10.32604/cmc.2022.031541



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1365

    View

  • 530

    Download

  • 0

    Like

Share Link