Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

Ayad Ghany Ismaeel, Krishnadas Janardhanan, Manishankar Sankar, Yuvaraj Natarajan, Sarmad Nozad Mahmood, Sameer Alani () and Akram H. Shather
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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

Sustainability, 2023, vol. 15, issue 19, 1-17

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.

Keywords: traffic pattern; classification; smart cities; recurrent neural network; accuracy; precision; recall; F1-score (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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