IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v647y2024ics0378437124003790.html
   My bibliography  Save this article

Short-term traffic flow prediction based on hybrid decomposition optimization and deep extreme learning machine

Author

Listed:
  • Zhao, Ke
  • Guo, Dudu
  • Sun, Miao
  • Zhao, Chenao
  • Shuai, Hongbo
  • Shao, Chunfu

Abstract

Precise and robust short-term traffic flow forecasting is vital to reduce carbon emissions and alleviate traffic congestion. However, developing a traffic flow prediction model that is both precise and robust is exceedingly difficult, owing to the nonlinear and non-stationary characteristics of traffic flow. This study proposes a novel hybrid model (CPQDELM) in pursuit of this objective. This model attains satisfactory prediction performance by integrating the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), dung beetle optimization algorithm based on quantum inspiration and multistrategy improvement(QMDBO), and deep extreme learning machine (DELM). The proposed model employs CEEMDAN and PE for data decomposition and reorganization and then uses the QMDBO algorithm to optimize the DELM parameters. Experiments are conducted in this study to validate the efficacy of the QMDBO algorithm and the CPQDELM hybrid model, respectively. First, the effectiveness of the improvement strategy proposed in this study is verified by comparing the proposed QMDBO algorithm with six other algorithms through experiments on the CEC2021 test function. Secondly, using two real-world datasets, comparing the CPQDELM hybrid model and twelve baseline models. The results indicate that the model proposed in this study performs better than the current methodologies. The RMSE, MAE, and MAPE of the model proposed in this study are all decreased by 27.24 %, 25.97 %, and 33.56 %, respectively, when compared to the conventional DELM, using the S321k51 dataset of mountain scenic highway as an example.

Suggested Citation

  • Zhao, Ke & Guo, Dudu & Sun, Miao & Zhao, Chenao & Shuai, Hongbo & Shao, Chunfu, 2024. "Short-term traffic flow prediction based on hybrid decomposition optimization and deep extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 647(C).
    • Handle: RePEc:eee:phsmap:v:647:y:2024:i:c:s0378437124003790
    DOI: 10.1016/j.physa.2024.129870
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124003790
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129870?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Whittaker, Joe & Garside, Simon & Lindveld, Karel, 1997. "Tracking and predicting a network traffic process," International Journal of Forecasting, Elsevier, vol. 13(1), pages 51-61, March.
    2. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    3. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    4. Wenguang Chai & Yuexin Zheng & Lin Tian & Jing Qin & Teng Zhou, 2023. "GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting," Mathematics, MDPI, vol. 11(16), pages 1-15, August.
    5. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    6. Hu, Guojing & Whalin, Robert W. & Kwembe, Tor A. & Lu, Weike, 2023. "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    7. Yang, Yuwei & Li, Zhuoxuan & Chen, Jun & Liu, Zhiyuan & Cao, Jinde, 2024. "TRELM-DROP: An impavement non-iterative algorithm for traffic flow forecast," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    2. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    3. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Yuan, Caichenran & Cang, Junjie & Zhang, Kai & Pecht, Michael, 2024. "Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model," Energy, Elsevier, vol. 298(C).
    4. Lu, Feng & Liu, Kang & Duan, Yingying & Cheng, Shifen & Du, Fei, 2018. "Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 227-237.
    5. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    6. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    7. Hu, Guojing & Whalin, Robert W. & Kwembe, Tor A. & Lu, Weike, 2023. "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    8. Wang, Wei & Zhang, Hanyu & Li, Tong & Guo, Jianhua & Huang, Wei & Wei, Yun & Cao, Jinde, 2020. "An interpretable model for short term traffic flow prediction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 264-278.
    9. Mumin Zhang & Yuzhi Wang & Haochen Zhang & Zhiyun Peng & Junjie Tang, 2023. "A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Wen, Tzai-Hung & Chin, Wei-Chien-Benny & Lai, Pei-Chun, 2017. "Understanding the topological characteristics and flow complexity of urban traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 166-177.
    12. Huayuan Chen & Zhizhe Lin & Yamin Yao & Hai Xie & Youyi Song & Teng Zhou, 2024. "Hybrid Extreme Learning for Reliable Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 12(20), pages 1-15, October.
    13. Shanjiang Zhu & David Levinson, 2011. "A Portfolio Theory of Route Choice," Working Papers 000096, University of Minnesota: Nexus Research Group.
    14. Alireza Ermagun & David M Levinson, 2019. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions," Environment and Planning B, , vol. 46(9), pages 1684-1705, November.
    15. Ai, Chunyu & He, Shan & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm," Energy, Elsevier, vol. 278(C).
    16. Kamarianakis, Yiannis & Prastacos, Poulicos, 2002. "Space-time modeling of traffic flow," ERSA conference papers ersa02p141, European Regional Science Association.
    17. Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
    18. Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019. "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, vol. 64(C), pages 159-171.
    19. Sun, Shaolong & Du, Zongjuan & Jin, Kun & Li, Hongtao & Wang, Shouyang, 2023. "Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy," Applied Energy, Elsevier, vol. 350(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:647:y:2024:i:c:s0378437124003790. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.