IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v585y2022ics0378437121007147.html
   My bibliography  Save this article

Short-term traffic prediction based on time series decomposition

Author

Listed:
  • Huang, Haichao
  • Chen, Jingya
  • Sun, Rui
  • Wang, Shuang

Abstract

Traffic flow decomposition is an alternative method to explore the composition of traffic flow and improve prediction accuracy. However, most of them suffer from the inability to fully utilize the character of traffic data. This paper presents a novel framework for traffic flow decomposition and modeling named Time Series Decomposition (TSD). The traffic flow is adaptively decomposed into periodic component, residual component and volatile component which are modeled respectively. Empirical Mode Decomposition (EMD) is applied to extract the intrinsic mode functions (IMFs) of traffic flow, the periodic patterns are intuitively presented via Hilbert transform in terms of frequencies. Then the periodic component can be described as a Fourier series based on obtained frequencies. Meanwhile, the residual component is presented by IMF with the lowest frequency. The remaining part is the volatile component modeled by supervised learning. The proposed hybrid model is evaluated on the real-world dataset and compared with classical baseline models. The results demonstrate that TSD can unearth the underlying periodic patterns and provide an explicable composition of the traffic flow. Furthermore, the volatile component ensures the accuracy of single-step prediction while periodic and residual components show promising abilities in improving the multi-step prediction accuracy of short-term traffic flow.

Suggested Citation

  • Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    • Handle: RePEc:eee:phsmap:v:585:y:2022:i:c:s0378437121007147
    DOI: 10.1016/j.physa.2021.126441
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121007147
    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.2021.126441?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. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    2. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    3. Tang, Jinjun & Chen, Xinqiang & Hu, Zheng & Zong, Fang & Han, Chunyang & Li, Leixiao, 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    5. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    6. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    7. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    2. Huang, Hai-chao & Chen, Jing-ya & Shi, Bao-cun & He, Hong-di, 2023. "Multi-step forecasting of short-term traffic flow based on Intrinsic Pattern Transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    3. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).
    4. Maosheng Li & Chen Zhang, 2024. "An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network," Sustainability, MDPI, vol. 16(2), pages 1-19, January.
    5. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    6. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Min Li & Mengshan Li & Bilong Liu & Jiang Liu & Zhen Liu & Dijia Luo, 2022. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    8. Chuang Yin & Nan Wei & Jinghang Wu & Chuhong Ruan & Xi Luo & Fanhua Zeng, 2024. "An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting," Energies, MDPI, vol. 17(2), pages 1-17, January.

    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. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Liu, Yang & Song, Yaolun & Zhang, Yan & Liao, Zhifang, 2022. "WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. 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).
    4. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    5. Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    6. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    7. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    8. Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    9. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    10. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).
    11. Hailin Zheng & Qinyou Hu & Chun Yang & Jinhai Chen & Qiang Mei, 2021. "Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    12. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    13. Fei Wang & Yinxi Liang & Zhizhe Lin & Jinglin Zhou & Teng Zhou, 2024. "SSA-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 12(12), pages 1-17, June.
    14. Shenghan Zhou & Chaofan Wei & Chaofei Song & Yu Fu & Rui Luo & Wenbing Chang & Linchao Yang, 2022. "A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    15. Zhai, Linbo & Yang, Yong & Song, Shudian & Ma, Shuyue & Zhu, Xiumin & Yang, Feng, 2021. "Self-supervision Spatiotemporal Part-Whole Convolutional Neural Network for Traffic Prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    16. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    17. Alisson Assis Cardoso & Flávio Henrique Teles Vieira, 2019. "Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-22, November.
    18. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.
    19. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    20. Fang, Weiwei & Zhuo, Wenhao & Yan, Jingwen & Song, Youyi & Jiang, Dazhi & Zhou, Teng, 2022. "Attention meets long short-term memory: A deep learning network for traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(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:585:y:2022:i:c:s0378437121007147. 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.