Data-Driven Estimation of Vessel Port Stay Time Using Conditional Multimodal Information

Vessel port stay time is a key indicator for berth allocation, crane planning, and short-term operational coordination in container terminals. However, existing prediction approaches often rely mainly on numerical operational data and assume complete information availability, limiting their reliability when localized visibility constraints or incomplete sensing occur. This study develops and evaluates an availability-aware multimodal prediction framework for vessel port stay time estimation. The framework adapts cross-attention-based fusion to integrate structured operational variables, numerical marine weather observations, and image-derived visibility information extracted from port monitoring images under incomplete monitoring image availability. In the framework, operational and numerical weather variables form the structured predictive state, whereas image-derived visibility information is conditionally incorporated as an auxiliary visual signal only when a matched and usable monitoring image is available. The proposed approach was evaluated using long-term vessel call data from a major container terminal. Compared with commonly used machine learning and deep learning baselines, the proposed model improved prediction accuracy, while residual analyses indicated reduced systematic prediction bias. These findings suggest that the proposed framework can support more reliable short-term berth planning under practical data-collection constraints.