Machine learning (ML) techniques are extensively applied to practical maritime transportation issues. Due to the difficulty and high cost of collecting large volumes of data in the maritime industry, in many maritime studies, ML models are trained with small training datasets. The relative predictive performances of these trained ML models are then compared with each other and with the conventional model using the same test set. The ML model that performs the best out of the ML models and better than the conventional model on the test set is regarded as the most effective in terms of this prediction task. However, in scenarios with small datasets, this common process may lead to an unfair comparison between the ML and the conventional model. Therefore, we propose a novel process to fairly compare multiple ML models and the conventional model. We first select the best ML model in terms of predictive performance for the validation set. Then, we combine the training and the validation sets to retrain the best ML model and compare it with the conventional model on the same test set. Based on historical port state control (PSC) inspection data, we examine both the common process and the novel process in terms of their ability to fairly compare ML models and the conventional model. The results show that the novel process is more effective at fairly comparing the ML models with the conventional model on different test sets. Therefore, the novel process enables a fair assessment of ML models' ability to predict key performance indicators in the context of limited data availability in the maritime industry, such as predicting the ship fuel consumption and port traffic volume, thereby enhancing their reliability for real-world applications.

Maritime transport plays an important role in global supply chain. To guarantee maritime safety, protect the marine environment, and enhance the living and working conditions of the seafarers, international codes and conventions are developed and implemented. Port state control (PSC) is a critical maritime policy to ensure that ships comply with the related regulations by selecting and inspecting foreign visiting ships visiting a national port. As the major inspection result, ship detention, which is an intervention action taken by the port state, is dependent on both deficiency/deficiencies (i.e., noncompliance) detected and the judgement of the inspector. This study aims to predict ship detention based on the number of deficiencies identified under each deficiency code and explore how each of them influences the detention decision. We innovatively view ship detention as a type of anomaly, which refers to data points that are few and different from the majority, and develop an isolation forest (iForest) model, which is an unsupervised anomaly detection model, for detention prediction. Then, techniques in explainable artificial intelligence are used to present the contribution of each deficiency code on detention. Numerical experiments using inspection records at the Hong Kong port are conducted to validate model performance and generate policy insights.

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.