High-speed railway express delivery volume forecast based on data-driven ensemble forecast approaches: The China case

Abstract

Current researches on logistics delivery volume forecast mainly focus on traditional transportation modes such as road, railway, and aviation, with little research on predicting high-speed railway express delivery (HSRED) volume. In this paper, a data-driven ensemble forecast approach is proposed to forecast the HSRED. Firstly, we use four kinds of single methods to predicate the HSRED, include Auto-Regressive Moving Average Model (ARMA), Linear Regression (LR), Random Forest (RF) and Singular Spectrum Analysis (SSA). Secondly, based on the predication results of single methods, we apply two ensemble methods including Arithmetic Mean (AM) and Allocation Integration Operator (AIO) to determine 22 combinations of ensemble forecast approaches. The Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Correlation Coefficient (CC) are used to evaluate the performance of each single and ensemble approach. Next, a real-world case study is conducted by using the HSRED in China as the background, the delivery demand data from Jan. 2014 to Mar. 2023 is used as the input. The results show that: The prediction results based on SSA, ARMA, and RF are close to the actual data. LR has the worst predication performance. The overall performance indicators of the ensemble models are better than those of the single prediction models. The ensemble models by using AIO are generally better than those by using AM. SSA-ARMA ensemble model constructed by AIO shows the best predictive performance. The trend of HSRED changes from 2025 to 2050 is gentle, and there will be no explosive growth, and it will gradually decrease and eventually stabilize. Finally, we distribute the predicted results to each province based on the gravity model and conduct visual analysis. The results show that: Provinces with high demand for HSRED include Shandong, Zhejiang, Jiangsu, Shanghai, and Beijing, mostly concentrated in the eastern and southeastern regions, we can consider strengthening the development and increasing investment of HSRED in these cities. Provinces with less demand, efforts can be made to strengthen publicity, accelerate the formation process of the high-speed railway network, and increase support for the HSRED industry.