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

Author

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  • 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
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    References listed on IDEAS

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    1. 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).
    2. 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).
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).

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