Application of Artificial Intelligence in Maritime Transportation

Dear Colleagues,

The use of cargo carriages is mainly implemented by ships worldwide as a manner of cost-effective transport. The recruitment of new ship crews has become difficult due to piracy, staying away from home for long periods of time, etc. The new development of artificial intelligence (AI) techniques demonstrates tremendous potential in alleviating the above-mentioned disadvantages. It can be envisioned that AI techniques may require fewer on-board ship crew members, whilst the maritime traffic safety and efficiency will be obviously enhanced as well. More specifically, AI techniques will be introduced to revolutionize the shipping industry by conducting the following tasks: maritime traffic situation awareness, automated ship controlling, optimal ship trajectory planning, ship-shore-vehicle collaboration, vehicle scheduling for automated container terminal (ACT), intelligent maritime supervision and management, multi-ship cooperative, etc.

The Special Issue aims to invite studies to effectively guide the future planning, design, construction, and application of maritime transportation system, and provides strong support for cultivating new-era maritime transportation industry with support of varied data sources (video, automatic identification system (AIS), radar, etc.). We invite full paper submissions fitting the general theme of “application of artificial intelligence in maritime transportation”. Moreover, we encourage submissions from a broad range of research fields related to maritime transportation issues. 

• Intelligent maritime traffic situation awareness. 
• Ship behavior identification and prediction via varied maritime traffic data sources.
• Optimal traffic controlling via cooperation of vehicle, ship and management center.
• Ship visual navigation and mooring via multiple maritime data sources.
• Maritime traffic safety analysis.
• Ship and vehicle fleet controlling and trajectory planning.
• Vehicle scheduling and optimization related issues in the ACT era.
• Carbon-emission-motivated trajectory planning, scheduling, and controlling, etc.
• Traffic flow prediction and analysis. 

Dr. Xinqiang Chen
Dr. Dongfang Ma
Dr. Ryan Wen Liu
Guest Editors

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