Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16361-d996251.html
   My bibliography  Save this article

A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Extreme Learning Machine and Improved Kernel Density Estimation

Author

Listed:
  • Leina Zhao

    (College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
    School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yujia Bai

    (College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China)

  • Sishi Zhang

    (College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yanpeng Wang

    (School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Jie Kang

    (School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Wenxuan Zhang

    (School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract
Short-term traffic flow prediction is the basis of and ensures intelligent traffic control. However, the conventional models cannot make accurate predictions due to the strong nonlinearity and randomness in short-term traffic flow data. To this end, the authors of this paper developed a novel hybrid model based on extreme learning machine (ELM), adaptive kernel density estimation (AKDE), and conditional kernel density estimation (CKDE). Specifically, the ELM model was employed for nonlinear prediction. Then, AKDE was established to estimate the bandwidth of CKDE (i.e., AKDE-CKDE), which predicted the training residuals obtained by ELM. Finally, the predicted results of the two models were superimposed to derive the final prediction of the hybrid model. Two case studies based on measured data were conducted to evaluate the performance of the proposed method. The experimental results indicate that the proposed method can realize a significant improvement in terms of forecasting accuracy in comparison with the other concerned models. For instance, it performed better than the single ELM model, with an improvement in the evaluation criterion of a mean relative percentage error of 7.46%.

Suggested Citation

  • Leina Zhao & Yujia Bai & Sishi Zhang & Yanpeng Wang & Jie Kang & Wenxuan Zhang, 2022. "A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Extreme Learning Machine and Improved Kernel Density Estimation," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16361-:d:996251
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16361/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/24/16361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qi-ming Wang & Ai-wan Fan & He-sheng Shi, 2017. "Network traffic prediction based on improved support vector machine," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 1976-1980, November.
    2. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
    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. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    2. Haben, Stephen & Giasemidis, Georgios, 2016. "A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1017-1022.
    3. Mateusz Ciski & Krzysztof Rząsa & Marek Ogryzek, 2019. "Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    4. Yang Hu & Yilin Qiao & Jingchun Chu & Ling Yuan & Lei Pan, 2019. "Joint Point-Interval Prediction and Optimization of Wind Power Considering the Sequential Uncertainties of Stepwise Procedure," Energies, MDPI, vol. 12(11), pages 1-21, June.
    5. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
    6. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
    7. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    8. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    9. Mohsen Vahidzadeh & Corey D. Markfort, 2019. "Modified Power Curves for Prediction of Power Output of Wind Farms," Energies, MDPI, vol. 12(9), pages 1-19, May.
    10. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
    11. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    12. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
    13. Jeon, Jooyoung & Taylor, James W., 2016. "Short-term density forecasting of wave energy using ARMA-GARCH models and kernel density estimation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 991-1004.
    14. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
    15. Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019. "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, vol. 84(C).
    16. Nasery, Praanjal & Aziz Ezzat, Ahmed, 2023. "Yaw-adjusted wind power curve modeling: A local regression approach," Renewable Energy, Elsevier, vol. 202(C), pages 1368-1376.
    17. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Rejoinder on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 45-50, March.
    18. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Mei Li & Ying Lin & Shuai Huang & Craig Crossland, 2016. "The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 86-97, January.
    19. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    20. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.

    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:gam:jsusta:v:14:y:2022:i:24:p:16361-:d:996251. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.