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Maritime transport forms the backbone of international logistics, as it allows for the transfer of bulk and long-haul products. The sophisticated planning required for this form of transportation frequently involves challenges such as unpredictable weather, diverse types of cargo kinds, and changes in port conditions, all of which can raise operational expenses. As a result, the accurate projection of a ship’s total time spent in port, and the anticipation of potential delays, have become critical for effective port activity planning and management. In this work, we aim to develop a port management system based on enhanced prediction and classification algorithms that are capable of precisely forecasting the lengths of ship stays and delays. On both the training and testing datasets, the XGBoost model was found to consistently outperform the alternative approaches in terms of RMSE, MAE, and R2 values for both the turnaround time and waiting period models. When used in the turnaround time model, the XGBoost model had the lowest RMSE of 1.29 during training and 0.5019 during testing, and also achieved the lowest MAE of 0.802 for training and 0.391 for testing. It also had the highest R2 values of 0.9788 during training and 0.9933 during testing. Similarly, in the waiting period model, the XGBoost model outperformed the random forest and decision tree models, with the lowest RMSE, MAE, and greatest R2 values in both the training and testing phases.

This paper aims at defining generic characteristics of dry ports by carrying out an analysis using a large sample of dry ports from around the world. The dataset includes details on 107 inland terminals worldwide. All dry ports in the database have been selected from studies in the extant literature before being shortlisted to fit our research scope. Data collected include terminologies used, actors driving the development, terminal throughput, total area, services provided and the relation with the corresponding seaport(s). Using statistical analysis, the paper examines how dry port parameters are influenced by (1) a different terminal set up, like sea-driven and land-driven development, developed and developing system, dry port functions; (2) specifications of the seaport with which the dry port is connected, i.e. seaport traffic, connectivity, utilization, etc. and (3) the transport leg linking dry ports and seaports. The findings could be applied to the planning and development of inland nodes from the perspectives of different stakeholders. [ABSTRACT FROM AUTHOR]