{"id":8921,"date":"2026-07-03T17:43:38","date_gmt":"2026-07-03T09:43:38","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8921"},"modified":"2026-07-10T13:57:20","modified_gmt":"2026-07-10T05:57:20","slug":"jase-202610-33-031","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-031","title":{"rendered":"Research on Urban Traffic Flow Prediction Based on Deep Recurrent Network and Bidirectional LSTM"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Jinsheng Wang1,2,3<\/sup>, Bo Liao1,3<\/sup><\/i><\/a>, Fangxiang Wu4<\/sup>, Zhihao Jiang5,6<\/sup>, and Fei Tian7<\/sup><\/p>\n\n\n\n

1<\/sup>School of Mathematics and Statistics, Hainan Normal University, Hainan Haikou 571158, China<\/p>\n\n\n\n

2<\/sup>Scientific Computing and Applied Mathematics Laboratory, Haikou University of Economics, Hainan Haikou 571127, China<\/p>\n\n\n\n

3<\/sup>Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Hainan Haikou
571158, China <\/p>\n\n\n\n

4<\/sup>Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon
SKS7N5A9, Canada<\/p>\n\n\n\n

5<\/sup>Public Teaching Department, Hainan Vocational University of Science and Technology, Hainan Haikou 571126, China<\/p>\n\n\n\n

6<\/sup>Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia<\/p>\n\n\n\n

7<\/sup>School of Financial Management, Hainan College of Econmics and Business, Hainan Haikou 571127, China<\/p>\n<\/div>\n\n\n\n

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Received: January 1, 2026
Accepted:\u00a0April 19, 2026
Publication Date:\u00a0July 3, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall flow of MA-DRN-BiLSTM approach<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.031<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Traffic flow (TF) forecasting plays a crucial role in modern urban transportation systems by enabling congestion mitigation and efficient traffic management. With the rapid growth of intelligent transportation systems, robust prediction models are required to process large-scale traffic data in real time. This study presents an enhanced urban traffic forecasting model based on Deep Recurrent Networks (DRN) integrated with Bidirectional Long
ShortTerm Memory (BiLSTM) networks to effectively capture temporal dependencies and spatial correlations in traffic data. Traffic datasets collected from sensors at major intersections include vehicle counts, speed, and congestion information across peak and off-peak periods. Noise reduction is performed using median filtering, followed by Min-Max normalization. Wavelet transform-based feature extraction captures multi-resolution traffic patterns. The proposed Malleable Aquila Optimized DRN-BiLSTM model achieves superior performance, attaining an R2<\/sup> of 0.97 , RMSE of 12.14, and MAE of 10.04, demonstrating its effectiveness for shortterm urban traffic forecasting and planning.<\/p>\n\n\n\n

Keywords: Urban Traffic Flow, Intelligent Transportation Systems, Malleable Aquila Optimized-DRN with BiLSTM, Traffic Management, Predictive Model<\/em><\/p>\n\n\n\n

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  1. [1] W. Shu, K. Cai, and N. N. Xiong, (2021) \u201cA short-term traffic flow prediction model based on an improved gated recurrent unit neural network\u201d IEEE Transactions on Intelligent Transportation Systems 23(9): 16654\u201316665. DOI: 10.1109\/TITS.2021.3094659.<\/li>\n
  2. [2] K. Meduri, G. S. Nadella, H. Gonaygunta, and S. S. Meduri, (2023) \u201cDeveloping a fog computing\u2013based AI framework for real-time traffic management and optimization\u201d International Journal of Sustainable Development in Computer Science 5(4): 1\u201324.<\/li>\n
  3. [3] A. Boukerche, Y. Tao, and P. Sun, (2020) \u201cArtificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems\u201d Computer Networks 182: 107484. DOI: 10.1016\/j.comnet.2020.107484.<\/li>\n
  4. [4] J. Culita, S. I. Caramihai, I. Dumitrache, M. A. Moisescu, and I. S. Sacala, (2020) \u201cA hybrid approach for urban traffic prediction and control in smart cities\u201d Sensors 20(24): 7209. DOI: 10.3390\/s20247209.<\/li>\n
  5. [5] S. Shafiei, A. S. Mihaita, H. Nguyen, C. Bentley, and C. Cai, (2020) \u201cShort-term traffic prediction under non-recurrent incident conditions integrating data-driven models and traffic simulation\u201d Proceedings of the Transportation Research Board Annual Meeting: 1\u201310.<\/li>\n
  6. [6] A. Almeida, S. Br\u00e1s, I. Oliveira, and S. Sargento, (2022) \u201cVehicular traffic flow prediction using deployed traffic counters in a city\u201d Future Generation Computer Systems 128: 429\u2013442. DOI: 10.1016\/j.future.2021.10.022.<\/li>\n
  7. [7] H. Ulvi, M. A. Yerlikaya, and K. Yildiz, (2024) \u201cUrban traffic mobility optimization model: A novel mathematical approach for predictive urban traffic analysis\u201d Applied Sciences 14(13): 5873. DOI: 10.3390\/app14135873.<\/li>\n
  8. [8] M. Sreelekha and M. Midhunchakkaravarthy, (2024) \u201cIntelligent transportation system for sustainable and efficient urban mobility: Machine learning approach for traffic flow prediction\u201d Lecture Notes in Computer Science: 399\u2013412. DOI: 10.1007\/978-981-97-1488-9_30.<\/li>\n
  9. [9] S. Lu, Q. Zhang, G. Chen, and D. Seng, (2021) \u201cA combined method for short-term traffic flow prediction based on recurrent neural networks\u201d Alexandria Engineering Journal 60(1): 87\u201394. DOI: 10.1016\/j.aej.2020.06.008.<\/li>\n
  10. [10] A. M. Nagy and V. Simon, (2021) \u201cImproving traffic prediction using congestion propagation patterns in smart cities\u201d Advanced Engineering Informatics 50: 101343. DOI: 10.1016\/j.aei.2021.101343.<\/li>\n
  11. [11] D. Liu and J. Liu, (2019) \u201cIntelligent planning of rain water drainage system in new urban areas considering the planning of road network\u201d Journal of Applied Science and Engineering 22(2): 315\u2013328. DOI: 10.6180\/jase.201906_22(2).0013.<\/li>\n
  12. [12] X. Yuan, J. Chen, and J. e. a. Yang, (2022) \u201cFedSTN: Graph representation\u2013driven federated learning for edge computing\u2013enabled urban traffic flow prediction\u201d IEEE Transactions on Intelligent Transportation Systems 24(8): 8738\u20138748. DOI: 10.1109\/TITS.2022.3157056.<\/li>\n
  13. [13] C. Tang, J. Sun, Y. Sun, M. Peng, and N. Gan, (2020) \u201cA general traffic flow prediction approach based on spatiotemporal graph attention\u201d IEEE Access 8: 153731\u2013153741. DOI: 10.1109\/ACCESS.2020.3018452.<\/li>\n
  14. [14] C. Chen, Y. Liu, L. Chen, and C. Zhang, (2022) \u201cBidirectional spatiotemporal adaptive transformer for urban traffic flow forecasting\u201d IEEE Transactions on Neural Networks and Learning Systems 34(10): 6913\u20136925. DOI: 10.1109\/TNNLS.2022.3183903.<\/li>\n
  15. [15] A. Navarro-Espinoza, O. R. L\u00f3pez-Bonilla, and E. E. a. Garc\u00eda-Guerrero, (2022) \u201cTraffic flow prediction for smart traffic lights using machine learning algorithms\u201d Technologies 10(1): 5. DOI: 10.3390\/technologies10010005.<\/li>\n
  16. [16] C. Chen, Z. Liu, S. Wan, J. Luan, and Q. Pei, (2020) \u201cTraffic flow prediction based on deep learning in internet of vehicles\u201d IEEE Transactions on Intelligent Transportation Systems 22(6): 3776\u20133789. DOI: 10.1109\/TITS.2020.3025856.<\/li>\n
  17. [17] K. Wang, C. Ma, and Y. e. a. Qiao, (2021) \u201cA hybrid deep learning model with 1D CNN\u2013LSTM\u2013attention networks for short-term traffic flow prediction\u201d Physica A 583: 126293. DOI: 10.1016\/j.physa.2021.126293.<\/li>\n
  18. [18] H. Peng, H. Wang, and B. e. a. Du, (2020) \u201cSpatiotemporal incidence dynamic graph neural networks for traffic flow forecasting\u201d Information Sciences 521: 277\u2013290. DOI: 10.1016\/j.ins.2020.01.043.<\/li>\n
  19. [19] B. Vijayalakshmi, K. Ramar, and N. Z. e. a. Jhanjhi, (2021) \u201cAn attention-based deep learning model for traffic flow prediction using spatiotemporal features toward sustainable smart cities\u201d International Journal of Communication Systems 34(3): e4609. DOI: 10.1002\/dac.4609.<\/li>\n
  20. [20] H. Qiu, Q. Zheng, M. Msahli, G. Memmi, M. Qiu, and J. Lu, (2020) \u201cTopological graph convolutional network\u2013based urban traffic flow and density prediction\u201d IEEE Transactions on Intelligent Transportation Systems 22(7): 4560\u20134569. DOI: 10.1109\/TITS.2020.3032882.<\/li>\n
  21. [21] S. Du, T. Li, X. Gong, and S. J. Horng, (2020) \u201cA hybrid method for traffic flow forecasting using multimodal deep learning\u201d International Journal of Computational Intelligence Systems 13(1): 85\u201397. DOI: 10.2991\/ijcis.d.200120.001.<\/li>\n
  22. [22] A. Ali, Y. Zhu, and M. Zakarya, (2021) \u201cExploiting dynamic spatiotemporal correlations for citywide traffic flow prediction using attention-based neural networks\u201d Information Sciences 577: 852\u2013870. DOI: 10.1016\/j.ins.2021.08.042.<\/li>\n
  23. [23] X. Zhang, S. Wen, L. Yan, J. Feng, and Y. Xia, (2024) \u201cA hybrid convolutional spatiotemporal recurrent network for traffic flow prediction\u201d The Computer Journal 67(1): 236\u2013252. DOI: 10.1093\/comjnl\/bxac171.<\/li>\n
  24. [24] X. Kong, W. Xing, X. Wei, P. Bao, J. Zhang, and W. Lu, (2020) \u201cSTGAT: Spatiotemporal graph attention networks for traffic flow forecasting\u201d IEEE Access 8: 134363\u2013134372. DOI: 10.1109\/ACCESS.2020.3011186.<\/li>\n
  25. [25] M. Lv, Z. Hong, L. Chen, T. Chen, T. Zhu, and S. Ji, (2020) \u201cTemporal multigraph convolutional network for traffic flow prediction\u201d IEEE Transactions on Intelligent Transportation Systems 22(6): 3337\u20133348. DOI: 10.1109\/TITS.2020.2983763.<\/li>\n
  26. [26] A. Essien, I. Petrounias, P. Sampaio, and S. Sampaio, (2021) \u201cA deep-learning model for urban traffic flow prediction with traffic events mined from Twitter\u201d World Wide Web 24(4): 1345\u20131368. DOI: 10.1007\/s11280-020-00800-3.<\/li>\n
  27. [27] C. Ma, G. Dai, and J. Zhou, (2021) \u201cShort-term traffic flow prediction for urban road sections based on time series analysis and LSTM-BiLSTM method\u201d IEEE Transactions on Intelligent Transportation Systems 23(6): 5615\u20135624. DOI: 10.1109\/TITS.2021.3055258.<\/li>\n
  28. [28] A. Sadeghi-Niaraki, P. Mirshafiei, M. Shakeri, and S. M. Choi, (2020) \u201cShort-term traffic flow prediction using modified Elman recurrent neural network optimized through a genetic algorithm\u201d IEEE Access 8: 217526\u2013217540. DOI: 10.1109\/ACCESS.2020.3039410.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,1483,6],"tags":[1645],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:  BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202610_33.031   Download PDF Traffic flow (TF) forecasting plays a crucial role in modern urban…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/8921"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8921"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}