Vessel arrival uncertainty in ports has become a very common problem worldwide. Although ship operators have to notify the Estimated Time of Arrival (ETA) at predetermined time intervals, they frequently have to update the latest ETA due to unforeseen circumstances. This causes a series of inconveniences that often impact on the efficiency of terminal operations, especially in the daily planning scenario. Thus, for our study we adopted a machine learning approach in order to provide a qualitative estimate of the vessel delay/advance and to help mitigate the consequences of late/early arrivals in port. Using data on delays/advances at the individual vessel level, a comparative study between two transshipment container terminals is presented and the performance of three algorithmic models is evaluated. Results of the research indicate that when the distribution of the outcome is bimodal the performance of the discrete models is highly relevant for acquiring data characteristics. Therefore, the models are not flexible in representing data when the outcome distribution exhibits unimodal behavior. Moreover, graphical visualisation of the importance-plots made it possible to underline the most significant variables which might explain vessel arrival uncertainty at the two European ports.
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