IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i9p3678-d353311.html
   My bibliography  Save this article

Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

Author

Listed:
  • Xinqiang Chen

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
    Institute of Atmospheric Sciences & Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China)

  • Jinquan Lu

    (Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China)

  • Jiansen Zhao

    (Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China)

  • Zhijian Qu

    (Electrical and Automation Engineering College, East China Jiaotong University, Nanchang 330013, China)

  • Yongsheng Yang

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Jiangfeng Xian

    (Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3678-:d:353311
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/9/3678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/9/3678/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Yu, Shaowei & Fu, Rui & Guo, Yingshi & Xin, Qi & Shi, Zhongke, 2019. "Consensus and optimal speed advisory model for mixed traffic at an isolated signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(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. Lianzhen Wang & Han Zhang & Lingyun Shi & Qingling He & Huizhi Xu, 2021. "Optimization Model of Regional Traffic Signs for Inducement at Road Works," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
    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. Qiang Luo & Xiaodong Zang & Xu Cai & Huawei Gong & Jie Yuan & Junheng Yang, 2021. "Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
    4. Lyuchao Liao & Zhiyuan Hu & Chih-Yu Hsu & Jinya Su, 2023. "Fourier Graph Convolution Network for Time Series Prediction," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    5. 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).
    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. Zibin Wei & Tao Peng & Sijia Wei, 2022. "A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    8. Pankaj Gupta & Mukesh Kumar Mehlawat & Faizan Ahemad, 2023. "Selection of renewable energy sources: a novel VIKOR approach in an intuitionistic fuzzy linguistic environment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3429-3467, April.
    9. Xianwang Li & Zhongxiang Huang & Saihu Liu & Jinxin Wu & Yuxiang Zhang, 2023. "Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)," Sustainability, MDPI, vol. 15(10), pages 1-30, May.
    10. 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).
    11. Yang Shao & Zhongbin Luo & Huan Wu & Xueyan Han & Binghong Pan & Shangru Liu & Christian G. Claudel, 2020. "Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    12. 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.

    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. Peng, Jiali & Shangguan, Wei & Peng, Cong & Chai, Linguo, 2024. "Uncertainty modeling of connected and automated vehicle penetration rate under mixed traffic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    2. 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.
    3. Li, Lixiang & Cheng, Rongjun & Ge, Hongxia, 2021. "New feedback control for a novel two-dimensional lattice hydrodynamic model considering driver’s memory effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    4. 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).
    5. He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    6. Chen, Jianzhong & Liang, Huan & Li, Jing & Xu, Zhaoxin, 2021. "A novel distributed cooperative approach for mixed platoon consisting of connected and automated vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    7. 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).
    8. 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.
    9. 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.
    10. Wu, Zhibei & Sun, Jitao & Xu, Ruihua, 2021. "Consensus-based connected vehicles platoon control via impulsive control method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    11. 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).
    12. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    13. 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).
    14. 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).
    15. Wu, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    16. Fang Zong & Meng Zeng & Yang Cao & Yixuan Liu, 2021. "Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field," Sustainability, MDPI, vol. 13(6), pages 1-13, March.
    17. Xin, Qi & Fu, Rui & Ukkusuri, Satish V. & Yu, Shaowei & Jiang, Rui, 2021. "Modeling and impact analysis of connected vehicle merging accounting for mainline random length tight-platoon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    18. Rasaizadi, Arash & Farzin, Iman & Hafizi, Fateme, 2022. "Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    19. 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).
    20. Tang, Jinjun & Zhang, Xinshao & Yu, Tianjian & Liu, Fang, 2021. "Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(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:gam:jsusta:v:12:y:2020:i:9:p:3678-:d:353311. 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.