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Stefan cel Mare
University of Suceava
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Computer Science
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2023 - 8

An Enhanced Time-dependent Traffic Flow Prediction in Smart Cities

SHOUAIB, M. See more information about SHOUAIB, M. on SCOPUS See more information about SHOUAIB, M. on IEEExplore See more information about SHOUAIB, M. on Web of Science, METWALLY, K. See more information about  METWALLY, K. on SCOPUS See more information about  METWALLY, K. on SCOPUS See more information about METWALLY, K. on Web of Science, BADRAN, K. See more information about BADRAN, K. on SCOPUS See more information about BADRAN, K. on SCOPUS See more information about BADRAN, K. on Web of Science
 
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Download PDF pdficon (2,455 KB) | Citation | Downloads: 450 | Views: 976

Author keywords
data analysis, intelligent transportation systems, Internet of Things, prediction algorithms, recurrent neural networks

References keywords
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

Abstract
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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.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 363] [SCOPUS Times Cited 539]


[2] M. Attaran, "The impact of 5G on the evolution of intelligent automation and industry digitization," Journal of Ambient Intelligence and Humanized Computing, pp. 1-17, 2021.
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[3] S. Nizetic, P. Solic, D. Lopez-de-Ipina Gonzalez-De, and L. Patrono, "Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future," Journal of cleaner production, vol. 274, no. 20, p. 122877, 2020.
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[CrossRef] [SCOPUS Times Cited 54]


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[CrossRef] [SCOPUS Times Cited 4]


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[CrossRef] [SCOPUS Times Cited 55]


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[CrossRef] [SCOPUS Times Cited 150]


[9] S. V. Kumar and L. Vanajakshi, "Short-term traffic flow prediction using seasonal ARIMA model with limited input data," European Transport Research Review, vol. 7, no. 21, pp. 1-9, 2015.
[CrossRef] [Web of Science Times Cited 391] [SCOPUS Times Cited 534]


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[CrossRef] [Web of Science Times Cited 151] [SCOPUS Times Cited 163]


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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 7]


[12] B. Priambodo, A. Ahmad, and R. A. Kadir, "Spatio-temporal K-NN prediction of traffic state based on statistical features in neighbouring roads," Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9059-9072, 2021.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 7]


[13] Q. Chen, W. Wang, X. Huang, and H.-n. Liang, "Attention-based recurrent neural network for traffic flow prediction," Journal of Internet Technology, vol. 21, no. 3, pp. 831-839, 2020.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 47]


[14] Y. Shao, Y. Zhao, F. Yu, H. Zhu, and J. Fang, "The traffic flow prediction method using the incremental learning-based CNN-LTSM model: the solution of mobile application," Mobile Information Systems, vol. 2021, no. 4, pp. 1-16, 2021.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 15]


[15] N. Shahid, M. A. Shah, A. Khan, C. Maple, and G. Jeon, "Towards greener smart cities and road traffic forecasting using air pollution data," Sustainable Cities and Society, vol. 72, p. 103062, 2021.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 48]


[16] N. U. Khan, M. A. Shah, C. Maple, E. Ahmed, and N. Asghar, "Traffic flow prediction: An intelligent scheme for forecasting traffic flow using air pollution data in smart cities with bagging ensemble," Sustainability, vol. 14, no. 7, p. 4164, 2022.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 19]


[17] R.-E. Precup, Gh. Duca, S. Travin, and I. Zinicovscaia, "Processing, neural network-based modeling of biomonitoring studies data and validation on Republic of Moldova data," Proceedings of the Romanian Academy Series A - Mathematics Physics Technical Sciences Information Science vol. 23, no. 4, pp. 399-406, 2022.

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[CrossRef]


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[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 106]


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References Weight

Web of Science® Citations for all references: 2,186 TCR
SCOPUS® Citations for all references: 4,364 TCR

Web of Science® Average Citations per reference: 87 ACR
SCOPUS® Average Citations per reference: 175 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-23 11:03 in 149 seconds.




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