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An Enhanced Time-dependent Traffic Flow Prediction in Smart CitiesSHOUAIB, M. , METWALLY, K. , BADRAN, K.
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data analysis, intelligent transportation systems, Internet of Things, prediction algorithms, recurrent neural networks
prediction(14), traffic(13), flow(10), smart(8), data(7), model(6), intelligent(5), neural(4), intelligence(4), information(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2023-08-31
Volume 23, Issue 3, Year 2023, On page(s): 67 - 74
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.03008
Web of Science Accession Number: 001062641900008
SCOPUS ID: 85172369475
Recently, smart cities have emerged as one of the most important global trends initiated by many countries. The rapid development of the Internet of Things (IoT) and 5G connectivity enables numerous smart system technologies. One of the most critical aspects of smart cities is Intelligent Transportation Systems (ITS) which allow for accurate traffic forecasting. Existing traffic forecasting approaches use simple models based on univariate historical datasets or fuse historical traffic data with other datasets, such as air pollution records. These techniques interpreted traffic data points as independent samples despite their time dependency, resulting in poor prediction accuracy. In this paper, we propose an enhanced traffic prediction approach using the sliding window technique. The proposed approach transforms the traffic data points into time-series sequence data using the sliding window technique. Moreover, the proposed approach exploits traffic and pollution datasets for multivariate traffic prediction. The proposed approach compares different machine/deep learning techniques for prediction and evaluates their accuracy compared to a baseline benchmark. The experimental results show that the proposed approach significantly enhances the evaluation metrics values for the tested machine and deep learning techniques by an average value of 36% and 24%, respectively, compared to the base-line benchmark.
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