Container Ship Loading and Unloading Volume Forecast Based on Route-Terminal Combination Entities: A Case Study of Shenzhen SCCT

Abstract

Accurate short-term container throughput volume (CTV) forecasting is crucial for optimizing yard management and port operational efficiency. However, conventional forecasting frameworks typically assume deterministic vessel-side container volumes, neglecting the lag and uncertainty inherent to Electronic Data Interchange (EDI) pre-allocation data in terminal operations. To address these critical gaps, we develop a short-term forecasting framework structured around fine-grained Route-Terminal combination entities, grounded in the real-world operational requirements of South China Container Terminals (SCCTs) in Shenzhen. To enhance predictive robustness against operational volatility, we propose an improved ensemble methodology “Per-item and Global Greedy Ensemble” strategy, which apply greedy ensemble algorithm in different entities and provides a fallback global ensemble for few-record entities. We conduct a comprehensive evaluation of various models, including statistical and state-space models, gradient boosting models, classic deep learning architectures, and time series language models (TSLMs) across the following dual granularities: micro-level entity scale and macro-level daily business aggregation. Experimental results reveal distinct model advantages based on container types as follows: for continuous laden container flows, the proposed per-item greedy ensemble shows competitive performance at the daily aggregation level, whereas tree-based models perform better for sparse and irregular container flows. Overall, the decomposed entity framework and per-item ensemble strategy provide terminal operators with actionable decision support to improve short-term scheduling flexibility and resource utilization in stochastic port environments.