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Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks

Author

Listed:
  • Chengguang Liu

    (Big Data Institute, Central South University, Changsha 410083, China)

  • Jiaqi Zhang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China)

  • Xixi Luo

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China)

  • Yulin Yang

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Chao Hu

    (Big Data Institute, Central South University, Changsha 410083, China)

Abstract

The construction of high-speed rail lines in China has drastically improved the freight capacity of conventional railways. However, due to recent national energy policy adjustments, rail freight volumes, consisting mostly of coal, ore, and other minerals, have declined. As a result, the corresponding changes in the supply and demand of goods and transportation have led to a gradual transformation of the railway freight market from a seller’s market to a buyer’s market. It is important to carry out a systematic analysis and a precise forecast of the demand for rail freight transport. However, traditional time series forecasting models often lack precision during drastic fluctuations in demand, while deep learning-based forecasting models may lack interpretability. This study combines grey relational analysis (GRA) and deep neural networks (DNN) to offer a more interpretable approach to predicting rail freight demand. GRA is used to obtain explanatory variables associated with railway freight demand, which improves the intelligibility of the DNN prediction. However, the high-dimension predictor variable can make training on DNN challenging. Inspired by deep autoencoders (DAE), we add a layer of an encoder to the GRA-DNN model to compress and aggregate the high-dimension input. Case studies conducted on Chinese railway freight from 2000 to 2018 show that the proven GRA-DAE-NN model is precise and easy to interpret. Comparative experiments with conventional prediction models ARIMA, SVR, FC-LSTM, DNN, FNN, and GRNN further validate the performance of the GRA-DAE-NN model. The prediction accuracy of the GRA-DAE-NN model is 97.79%, higher than that of other models. Among the main explanatory variables, coal, oil, grain production, railway locomotives, and vehicles have a significant impact on the railway freight demand trend. The ablation experiment verified that GRA has a significant effect on the selection of explanatory variables and on improving the accuracy of predictions. The method proposed in this study not only accurately predicts railway freight demand but also helps railway transportation companies to better understand the key factors influencing demand changes.

Suggested Citation

  • Chengguang Liu & Jiaqi Zhang & Xixi Luo & Yulin Yang & Chao Hu, 2023. "Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9652-:d:1172521
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    References listed on IDEAS

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    1. Li, Qinglin & Rezaei, Jafar & Tavasszy, Lori & Wiegmans, Bart & Guo, Jingwei & Tang, Yinying & Peng, Qiyuan, 2020. "Customers’ preferences for freight service attributes of China Railway Express," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 225-236.
    2. Brian Kenji Iwana & Seiichi Uchida, 2021. "An empirical survey of data augmentation for time series classification with neural networks," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    3. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Guoqing An & Ziyao Jiang & Libo Chen & Xin Cao & Zheng Li & Yuyang Zhao & Hexu Sun, 2021. "Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    5. Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.
    6. Tianyang Wang & Abd E.I.-Baset Hassanien, 2021. "An Intelligent Passenger Flow Prediction Method for Pricing Strategy and Hotel Operations," Complexity, Hindawi, vol. 2021, pages 1-11, March.
    7. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    8. Chiung-Yu Huang & Chia-Chin Hsu & Mu-Lin Chiou & Chun-I Chen, 2020. "The main factors affecting Taiwan’s economic growth rate via dynamic grey relational analysis," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    9. Wu, Weitiao & Li, Peng & Liu, Ronghui & Jin, Wenzhou & Yao, Baozhen & Xie, Yuanqi & Ma, Changxi, 2020. "Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: A newsvendor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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