دورية أكاديمية
An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network
| العنوان: | An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network |
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| المؤلفون: | Maosheng Li, Chen Zhang |
| المصدر: | Sustainability, Vol 16, Iss 2, p 607 (2024) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | short-term section flow prediction, time series decomposition, countermeasure neural network, graph convolutional neural network, geo, archi |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2071-1050 |
| العلاقة: | https://doaj.org/article/3724bc65bf874ad890313f8029870a26Test |
| DOI: | 10.3390/su16020607 |
| الإتاحة: | https://doi.org/10.3390/su16020607Test https://doaj.org/article/3724bc65bf874ad890313f8029870a26Test |
| حقوق: | undefined |
| رقم الانضمام: | edsbas.A891F671 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20711050 |
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| DOI: | 10.3390/su16020607 |