Year: 2023
Type: article
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.
Source: Sustainability
DOI: 10.3390/su151914522
Authors: Ayad Ghany Ismaeel, Krishnadas Janardhanan, Manishankar Sankar, Yuvaraj Natarajan, Sarmad Nozad Mahmood, Sameer Alani, Akram H. Shather
Institutions: Northern Technical University, University of Anbar
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,447
OpenAlex ID: W4387422716