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

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-09-08
Major/Subject
Mcode
Degree programme
Language
en
Pages
23
Series
Journal of Marine Science and Engineering, Volume 9, issue 9
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.
Description
Funding Information: Funding: The research was funded by National Natural Science Foundation of China (grant num‐ ber: 72071041), Transportation Science and Technology Demonstration Project of Jiangsu Province, (grant number: 2018Y02), and China Society of Logistic (grant number: 2021CSLKT3‐096). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
Freight volume forecast, Inland ports, Machine learning, Spatial-temporal dependence, Time series analysis
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Citation
Liu , L , Zhang , Y , Chen , C , Hu , Y , Liu , C & Chen , J 2021 , ' 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 ' , Journal of Marine Science and Engineering , vol. 9 , no. 9 , 985 . https://doi.org/10.3390/jmse9090985