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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Author

Listed:
  • Ayad Ghany Ismaeel

    (Computer Technology Engineering, College of Engineering Technology, Al-Kitab University, Kirkuk 36001, Iraq)

  • Krishnadas Janardhanan

    (Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Kodakara, Thrissur 680684, India)

  • Manishankar Sankar

    (Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Kodakara, Thrissur 680684, India)

  • Yuvaraj Natarajan

    (Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore 641062, India)

  • Sarmad Nozad Mahmood

    (Electronic and Control Engineering Techniques Technical Engineering College, Northern Technical University, Kirkuk 36001, Iraq)

  • Sameer Alani

    (Computer Center, University of Anbar, Ramadi 55431, Iraq)

  • Akram H. Shather

    (Department of Computer Engineering Technology, Al-Kitab University, Altun Kopru, Kirkuk 36001, Iraq)

Abstract

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

Suggested Citation

  • Ayad Ghany Ismaeel & Krishnadas Janardhanan & Manishankar Sankar & Yuvaraj Natarajan & Sarmad Nozad Mahmood & Sameer Alani & Akram H. Shather, 2023. "Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network," Sustainability, MDPI, vol. 15(19), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14522-:d:1254547
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    References listed on IDEAS

    as
    1. Xinlan Guo & Mohammadamin Shirkhani & Emad M. Ahmed, 2022. "Machine-Learning-Based Improved Smith Predictive Control for MIMO Processes," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    2. Sura Mahmood Abdullah & Muthusamy Periyasamy & Nafees Ahmed Kamaludeen & S. K. Towfek & Raja Marappan & Sekar Kidambi Raju & Amal H. Alharbi & Doaa Sami Khafaga, 2023. "Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    3. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    Full references (including those not matched with items on IDEAS)

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