Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic

Abstract

The COVID-19 pandemic had a significant impact on container transportation. Accurate forecasting of container throughput is critical for policymakers and port authorities, especially in the context of the anomalous events of the COVID-19 pandemic. In this paper, we firstly proposed hybrid models for univariate time series forecasting to enhance prediction accuracy while eliminating the nonlinearity and multivariate limitations. Next, we compared the forecasting accuracy of different models with various training dataset extensions and forecasting horizons. Finally, we analysed the impact of the COVID-19 pandemic on container throughput forecasting and container transportation. An empirical analysis of container throughputs in the Yangtze River Delta region was performed for illustration and verification purposes. Error metrics analysis suggests that SARIMA-LSTM₂ and SARIMA-SVR₂ (configuration 2) have the best performance compared to other models and they can better predict the container traffic in the context of anomalous events such as the COVID-19 pandemic. The results also reveal that, with an increase in the training dataset extensions, the accuracy of the models is improved, particularly in comparison with standard statistical models (i.e. SARIMA model). An accurate prediction can help strategic management and policymakers to better respond to the negative impact of the COVID-19 pandemic.

Keywords: COVID-19 pandemic; Hybrid model; Machine learning model; SARIMA model; Yangtze River Delta multi-port region.