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Journal article
An Enhanced TimesNet-SARIMA Model for Predicting Outbound Subway Passenger Flow with Decomposition Techniques
Applied sciences, v 15(6), 2874
07 Mar 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
The accurate prediction of subway passenger flow is crucial for managing urban transportation systems. This research introduces a hybrid forecasting approach that combines an enhanced TimesNet model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Variational Mode Decomposition (VMD) to improve passenger flow prediction. The method decomposes time series data into Intrinsic Mode Functions (IMFs) using VMD, followed by adaptive predictions for each IMF with TimesNet and SARIMA. The dataset spans from 1 January to 25 January 2019, encompassing 70 million records processed into five-minute intervals. The results show that the VMD preprocessing effectively extracts features, enhancing prediction performance (13.25% MAE, 19.7% RMSE improvements). The hybrid method excels during peak times (52.75% MAE, 50.61% RMSE improvements) and outperforms baseline models like Informer and Crossformer, achieving 66.14% and 63.24% improvements in the MAE and RMSE, respectively. This research offers a reliable tool for predicting subway passenger flow, supporting the smart evolution of urban transport systems.
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Details
- Title
- An Enhanced TimesNet-SARIMA Model for Predicting Outbound Subway Passenger Flow with Decomposition Techniques
- Creators
- Tianzhou Zuo - Beijing Union UniversityShaohu Tang - Beijing Union UniversityLiang Zhang (Corresponding Author) - Drexel University, Engineering ManagementHailin Kang - Beijing Union UniversityHongkang Song - Beijing Union UniversityPengyu Li - Beijing Union University
- Publication Details
- Applied sciences, v 15(6), 2874
- Publisher
- MDPI
- Number of pages
- 25
- Grant note
- China National Key RD ProgramR&D Program of Beijing Municipal Education Commission: KM202111417003
This research was funded by following projects: the China National Key R&D Program (Grant No. 2021YFB1715700) and the R&D Program of Beijing Municipal Education Commission (KM202111417003).
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Management
- Web of Science ID
- WOS:001453511200001
- Scopus ID
- 2-s2.0-105001027424
- Other Identifier
- 991022032270804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary
- Engineering, Multidisciplinary
- Materials Science, Multidisciplinary
- Physics, Applied