Spatio-temporal and operational clustering of maritime container terminal activities for scenario-based truck appointment planning

Abstract

In today's uncertain and rapidly evolving global landscape, maritime container terminals are increasingly affected by operational disruptions such as yard congestion, unbalanced resources utilization and delays in container handling. Truck Appointment Systems (TAS) have emerged as a key strategy to regulate truck arrivals and smooth peak demand, yet their effectiveness remains limited by insufficient integration with yard-side dynamics. Appointment allocation is typically designed without accounting for the spatial and temporal variability of yard operations. Designing a robust TAS requires the definition of realistic and representative operational scenarios. This paper proposes an enhanced DBSCAN-based clustering framework designed to identify recurring operational scenarios in container terminals. This is performed by jointly analysing spatiotemporal truck arrival patterns and container handling behaviours. The algorithm extends traditional density-based clustering by incorporating multi-dimensional distance metrics that capture spatial proximity, temporal alignment and operational similarity between container movements. The application of the proposed approach to a real-world case study from an Italian container terminal demonstrates its ability to extract five recurrent operational scenarios, covering more than 90\% of container movements, with a noise ratio of 9.7\% and a High-Density Score (HDS) of 0.4253, indicating a good balance between cluster cohesion, coverage, and operational interpretability. The identified scenarios were further analysed and qualitatively validated through interactions with terminal planners and operational managers. Overall, the resulting clusters provide actionable insights to support robust TAS design, enabling the development of data-driven decision support tools that explicitly account for operational variability and enhance the resilience of terminal planning and management.