How to accurately calculate ship exhaust emissions has become urgent needs. In this paper, multi-source maritime information is integrated to estimate ship exhaust emissions under ocean environment. Influences of wind, wave and current on ship speed are firstly analyzed and mathematically modeled. Based on the influences, ocean environment information and ship trajectories are integrated to identify ship activities exactly. After that, ship activity based calculation method is present to obtain exhaust emissions from ship in various activities. Contribution ratios of different ship type and ship activities have been further discussed. In a case study of Ningbo-Zhoushan port in China, greenhouse gas (CO2, CO, SOx, NOx and PM) emissions from ships in 2014 calculated by the proposed method are 8.72 × 105 ton, 2.07 × 103 ton, 1.47 × 104 ton, 2.60 × 104 ton and 1.40 × 103 ton respectively. The maximum error is under 10%. Experimental results illustrate that the proposed method can produce more accurate ship exhaust emissions than traditional method under ocean environment conditions.@en

In the inland navigation environment, the serious AIS signal congestion and loss result in the ship AIS trajectory to be sparse and discontinuous. Traditional numerical analysis method is used to fit the lost trajectory of ships, and it cannot adapt to the complicated inland waterways with complicated bend, and is prone to cross the land coast. In this paper, a trajectory estimation method for inland river ship was proposed based on navigation experience. Based on the spatial consistency, time consistency and heading consistency, the history similar ship trajectories were extracted for neighbor sampling points of sparse trajectory, and the navigation experience was estimated by the method of Kernel density estimation, and navigational heat distribution map was modelled. And then the optimal route was calculated as approximate estimation trajectory in consideration of ship movement trend based on navigation heat. Three typical inland river channel scenarios were selected to carry out sparse trajectory restoration experiments. Results show that the proposed method can adapt well to the complex terrain of the inland waterway.@en

Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management.@en

In recent years, with the development of shipping industry, the mass of greenhouse gases (GHG) emissions originating from ships is also increasing rapidly. To reduce the GHG emissions from ships, designing the optimised routes with higher energy use efficiency is becoming more important for ships navigating in short seas. Based on the innovative application of energy efficiency operational index (EEOI), a route optimisation model is established. To solve the model, we divide one single voyage into several voyage segments and consider the variable speed across voyage segments. Then the navigation strategy is used to find out the sailing route of the minimum EEOI value, this sailing route is called as the green route. Finally, numerical experimental results show that the EEOI values of the experimental groups which used the optimised navigation method are 4.67%-16.95% lower than that of the control group. The results demonstrate that the green route designed in this paper can effectively improve the energy use efficiency of ships.@en

With the growth of traffic density, traditional collision risk models cannot thoroughly describe the complex traffic any more. Therefore, in this paper, a marine traffic situation complexity model, which is based on the relationship between ships, is introduced to metric the complexity of vessel traffic. Firstly, traffic density factor and traffic conflict factors are considered to construct the traffic unit complexity model, reflecting the traffic complexity and traffic characteristics; Secondly, by extending the traffic unit complexity model into the own-ship complexity model, the influences of the positions of other ship on the complexity spatial distribution are analysed; Finally, two scenarios are imported to validate the availability of the complexity model. The verification results show that the complexity can respond to different route structures and traffic situations sensitively.@en