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An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network

Author

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  • Maosheng Li

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Chen Zhang

    (Smart Transportation Key Laboratory of Hunan Province, Central South University, No. 22 South Shaoshan Road, Changsha 410075, China)

Abstract

Urban metro cross-section flow is the passenger flow that travels through a metro section. Its volume is a critical parameter for planning operation diagrams and improving the service quality of urban subway systems. This makes it possible to better plan the drive for the sustainable development of a city. This paper proposes an improved model for predicting urban metro section flow, combining time series decomposition and a generative adversarial network. First, an urban metro section flow sequence is decomposed using EMD (Empirical Mode Decomposition) into several IMFs (Intrinsic Mode Functions) and a trend function. The sum of all the IMF components is treated as the periodic component, and the trend function is considered the trend component, which are fitted by Fourier series function and spline interpolation, respectively. By subtracting the sum of the periodic and trend components from the urban metro section flow sequence, the error is regarded as the residual component. Finally, a GAN (generative adversarial network) based on the fusion graph convolutional neural network is used to predict the new residual component, which considers the spatial correlation between different sites of urban metro sections. The Chengdu urban metro system data in China show that the proposed model, through incorporating EMD and a generative adversarial network, achieves a 15–20% improvement in prediction accuracy at the cost of a 10% increase in the calculation time, meaning it demonstrates good prediction accuracy and reliability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:607-:d:1316585
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    References listed on IDEAS

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    1. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    2. 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).
    3. 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.
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