Applying Semi‐AutoML for Vessel Traffic Flow Prediction: Case Studies of Two Ports

Accurate prediction of vessel traffic flow plays a crucial role in maritime supply chain operations, enabling efficient resource allocation and timely delivery of goods. This study contributes to the field of maritime logistics and transportation management by employing a semiautomated machine learning (semi‐AutoML) approach and presenting a comparative analysis to predict vessel traffic flow in two distinct port settings. The proposed approach involves automatically evaluating the performance of a set of preselected models to identify the best‐fitting models for the dataset. This is followed by a manual tuning phase to further optimize the performance of the selected models. The proposed methodology is implemented on limited‐scale datasets from two separate case studies: Mohammedia port and Los Angeles port, with the latter serving as a benchmark against an existing study. The performance of the models in predicting vessel traffic flow was evaluated using different metrics. The findings indicate improvements in forecast accuracy, with an RMSE of 2.22 for Mohammedia port and 11.65 for Los Angeles port. The results for Los Angeles port showcase a notable improvement of up to 70.95% in RMSE compared to the outcomes of a previous study, emphasizing the superior efficacy of the proposed methodology in predicting vessel traffic flow. The workflow presented in this study was implemented using the PyCaret framework, and the Python code implementation is publicly available on Colab (https://colab.research.google.com/drive/1Wk5Y_1uFSEYJLx49nXPaoPnGUgLGY6cT).