Fine-Grained Forecasting of Short-Haul Logistics Volume Based on Time Series Feature Engineering and XGBoost Algorithm
TL;DR
This model successfully achieves cargo volume extrapolation at 10-minute granularity for the next 24 hours, accurately capturing the bimodal characteristics of cargo volume on specific routes, laying a solid data foundation for subsequent dynamic scheduling optimization.
Abstract
: Addressing the demand for high-frequency response efficiency in modern short-haul logistics networks, this study investigates fine-grained forecasting of short-haul logistics volume. First, historical logistics volume data undergoes preprocessing to screen target routes and eliminate outliers, while a custom function enables scientific segmentation of production periods across dates. Subsequently, periodic decomposition techniques were applied to decompose the raw time series into trend, seasonal, and residual components, confirming the pronounced cyclical patterns in logistics demand and the stationarity of residuals. During modeling, a sliding-window feature matrix was constructed to convert time-series data into supervised learning format. The XGBoost gradient boosting algorithm was employed, with the objective function optimized via second-order Taylor expansion to effectively capture nonlinear characteristics in freight volume sequences. The results show that the model achieves a coefficient of determination of 0.94 on the independent test set, with an average absolute percentage error of only 4%, demonstrating outstanding fitting capability and generalization performance. Ultimately, the model successfully achieves cargo volume extrapolation at 10-minute granularity for the next 24 hours, accurately capturing the bimodal characteristics of cargo volume on specific routes, laying a solid data foundation for subsequent dynamic scheduling optimization.