Recognizing the specific complexities of vessel traffic flow,this comprehensive survey exclusively addresses the predictive modelling in maritime transportation,tracing the evolution from conventional statistical appr...Recognizing the specific complexities of vessel traffic flow,this comprehensive survey exclusively addresses the predictive modelling in maritime transportation,tracing the evolution from conventional statistical approaches to modern artificial intelligence(AI)techniques.The survey examines a broad range of predictive targets,including vessel volume,trajectories,velocities,destinations and traffic patterns.Through bibliometric analysis utilizing Citespace,the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed.Our analysis indicates a clear trend towards AI-based models,highlighting their increasing dominance in enhancing predictive accuracy and efficiency.Additionally,we highlight persistent challenges,such as the integration of large datasets with traffic flow models and the critical need for real-time data analytics.The survey concludes with insights into the future of vessel traffic flow prediction research,emphasizing the potential of hybrid models that combine deep learning with statistical learning to enable more sophisticated predictive analytics to be performed.This review aims to serve as a guide for both academics and practitioners looking to maximize the use of predictive modelling in the maritime traffic sector.展开更多
基金supported in part by the National Key Research and Development Program of China(Grant No.2023YFC3010803)in part by the National Natural Science Foundation of China(Grant Nos.52272424 and 52372320)+1 种基金in part by the Key Research and Development Program of Hubei Province of China(Grant No.2023BCB123)in part by the Fundamental Research Funds for the Central Universities(Grant No.104972024KFYd0039).
文摘Recognizing the specific complexities of vessel traffic flow,this comprehensive survey exclusively addresses the predictive modelling in maritime transportation,tracing the evolution from conventional statistical approaches to modern artificial intelligence(AI)techniques.The survey examines a broad range of predictive targets,including vessel volume,trajectories,velocities,destinations and traffic patterns.Through bibliometric analysis utilizing Citespace,the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed.Our analysis indicates a clear trend towards AI-based models,highlighting their increasing dominance in enhancing predictive accuracy and efficiency.Additionally,we highlight persistent challenges,such as the integration of large datasets with traffic flow models and the critical need for real-time data analytics.The survey concludes with insights into the future of vessel traffic flow prediction research,emphasizing the potential of hybrid models that combine deep learning with statistical learning to enable more sophisticated predictive analytics to be performed.This review aims to serve as a guide for both academics and practitioners looking to maximize the use of predictive modelling in the maritime traffic sector.
