Is the spatial-temporal dependence model reliable for the short-term freight volume forecast of inland ports? A case study of the Yangtze River, China

Lei Liu, Yong Zhang*, Chen Chen, Yue Hu, Cong Liu, Jing Chen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR- GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.

Original languageEnglish
Article number985
Number of pages23
JournalJournal of Marine Science and Engineering
Volume9
Issue number9
DOIs
Publication statusPublished - 8 Sep 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Freight volume forecast
  • Inland ports
  • Machine learning
  • Spatial-temporal dependence
  • Time series analysis

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