| 3/2023 - 8 |
An Enhanced Time-dependent Traffic Flow Prediction in Smart CitiesSHOUAIB, M.   , METWALLY, K. , BADRAN, K. |
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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
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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| [1] G. Aceto, V. Persico, and A. Pescape, "Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0," Journal of Industrial Information Integration, vol. 18, no. 100129, pp. 1-14, 2020. [CrossRef] [Web of Science Times Cited 375] [SCOPUS Times Cited 569] [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. [CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 160] [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. [CrossRef] [Web of Science Times Cited 272] [SCOPUS Times Cited 490] [4] M. A. Jan, X. He, H. Song, and M. Babar, "Machine learning and big data analytics for IoT-enabled smart cities," Mobile Networks and Applications, vol. 26, pp. 156-158, 2021. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 8] [5] P. S. Saarika, K. Sandhya, and T. Sudha, "Smart transportation system using IoT," in 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017, pp. 1104-1107. [CrossRef] [SCOPUS Times Cited 56] [6] A. Elliethy and G. Sharma, "A joint approach to vector road map registration and vehicle tracking for wide area motion imagery," in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, pp. 1100-1104. [CrossRef] [SCOPUS Times Cited 4] [7] H. Dong, L. Jia, X. Sun, C. Li, and Y. Qin, "Road traffic flow prediction with a time-oriented ARIMA model," in 2009 Fifth International Joint Conference on INC, IMS and IDC, 2009, pp. 1649-1652. [CrossRef] [SCOPUS Times Cited 59] [8] C. Chen, J. Hu, Q. Meng, and Y. Zhang, "Short-time traffic flow prediction with ARIMA-GARCH model," in 2011 IEEE Intelligent Vehicles Symposium (IV), 2011, pp. 607-612. [CrossRef] [SCOPUS Times Cited 152] [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 410] [SCOPUS Times Cited 552] [10] Y. Qi and S. Ishak, "A Hidden Markov Model for short term prediction of traffic conditions on freeways," Transportation Research Part C: Emerging Technologies, vol. 43, pp. 95-111, 2014. [CrossRef] [Web of Science Times Cited 154] [SCOPUS Times Cited 168] [11] S.-x. Zhao, H.-w. Wu, and C.-r. Liu, "Traffic flow prediction based on optimized Hidden Markov model," in Journal of Physics: Conference Series, 2019, vol. 1168, p. 052001. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9] [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 36] [SCOPUS Times Cited 49] [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 36] [SCOPUS Times Cited 52] [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 14] [SCOPUS Times Cited 22] [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. [18] M. Z. Naser and A. H. Alavi, "Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences," Architecture, Structures and Construction, pp. 1-19, 2021. [CrossRef] [19] M. I. Ali, F. Gao, and A. Mileo, "CityBench: A configurable benchmark to evaluate RSP engines using smart city datasets," In proceedings of ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, 2015, pp. 374-389. [CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 105] [20] A. Boukerche, Y. Tao, and P. Sun, "Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems," Computer Networks, vol. 182, no. 6, p. 107484, 2020. [CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 113] [21] N. I. Sapankevych and R. Sankar, "Time series prediction using support vector machines: A survey," in IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 24-38, 2009. [CrossRef] [Web of Science Times Cited 631] [SCOPUS Times Cited 861] [22] R. Fu, Z. Zhang, and L. Li, "Using LSTM and GRU neural network methods for traffic flow prediction," 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324-328, 2016. [CrossRef] [SCOPUS Times Cited 1086] [23] V., Amit, T. Meenpal, and B. Acharya, "Computational cost reduction of convolution neural networks by insignificant filter removal," Romanian Journal of Information Science and Technology, vol. 25, no. 2, pp. 150-165, 2022. [24] J. Wang, X. Niu, L. Zhang, and M. Lv, "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, vol. 73, p. 102222, 2021. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
