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

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:32:26Z
Fecha de publicación2026-01-01
ResumenIn 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.es_ES
Doihttps://doi.org/10.1016/j.martra.2026.100148es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/4275
Idiomaenes_ES
EditorialELSEVIERes_ES
RelaciónMaritime Transport Researches_ES
URL relacionadohttps://doi.org/10.1016/j.martra.2026.100148es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteMaritime Transport Research
Palabra claveTruck appointment systemses_ES
Palabra claveClusteringes_ES
Palabra claveTerminal operations analysises_ES
Palabra claveOperational scenarios discoveringes_ES
Palabra claveMultivariate spatiotemporal and operationales_ES
Palabra claveclusteringes_ES
Palabra claveDBSCANes_ES
Palabra claveCongestiones_ES
TítuloSpatio-temporal and operational clustering of maritime container terminal activities for scenario-based truck appointment planninges_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorCaballini, Claudia
AutorMar-Ortiz, Julio
AutorGracia, Maria D.
AutorCaballini, Claudiaes_ES
AutorMar-Ortiz, Julioes_ES
AutorGracia, Maria D.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
URL relacionadahttps://doi.org/10.1016/j.martra.2026.100148
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen10es_ES

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