Automatic Identification Systems Data Research Articles

AIS data to inform small scale fisheries management and marine spatial planning

Automatic Identification Systems (AIS) are collision avoidance devices used on-board both commercial and leisure craft. These systems report the position, track and speed of the vessel through Very High Frequency radio transmissions which are accessible to any suitable receiver. This paper explores the potential to use AIS data to inform small scale fisheries management and marine spatial planning. First, the propagation and reception of the line of sight AIS transmissions was modelled around the coast of Scotland to identify areas where the use of AIS may be compromised. Using open source Geographic Information System and relational database software, computationally efficient methods of processing and analysing AIS data were explored. Three months of AIS data derived from 274 Scottish small scale fishing vessels were used to provide spatio-temporal analyses of trip duration and distance travelled, location of fishing activities, and vessel dependency on fishing grounds. The coverage, opportunities and challenges of using AIS are discussed together with broader applications and future developments.

Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable individual vessel accident risk levels and shoreline contamination risk from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – the Portuguese continental shelf – with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time, or as an alternative, a correction factor based on vessel distance from coast. Shoreline risks can be computed in real time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns (“hot spots”) or developing sensitivity analysis to specific conditions, whereas real-time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

An AIS-based approach to calculate atmospheric emissions from the UK fishing fleet

The fishing industry is heavily reliant on the use of fossil fuel and emits large quantities of greenhouse gases and other atmospheric pollutants. Methods used to calculate fishing vessel emissions inventories have traditionally utilised estimates of fuel efficiency per unit of catch. These methods have weaknesses because they do not easily allow temporal and geographical allocation of emissions. A large proportion of fishing and other small commercial vessels are also omitted from global shipping emissions inventories such as the International Maritime Organisation's Greenhouse Gas Studies. This paper demonstrates an activity-based methodology for the production of temporally- and spatially-resolved emissions inventories using data produced by Automatic Identification Systems (AIS). The methodology addresses the issue of how to use AIS data for fleets where not all vessels use AIS technology and how to assign engine load when vessels are towing trawling or dredging gear. The results of this are compared to a fuel-based methodology using publicly available European Commission fisheries data on fuel efficiency and annual catch. The results show relatively good agreement between the two methodologies, with an estimate of 295.7 kilotons of fuel used and 914.4 kilotons of carbon dioxide emitted between May 2012 and May 2013 using the activity-based methodology. Different methods of calculating speed using AIS data are also compared. The results indicate that using the speed data contained directly in the AIS data is preferable to calculating speed from the distance and time interval between consecutive AIS data points.

Optimising end-to-end maritime supply chains: a carbon footprint perspective

Purpose – The purpose of this paper is to illustrate an optimisation method, and resulting insights, for minimising total logistics-related carbon emissions for end-to-end supply chains. Design/methodology/approach – The research is based on two real-life UK industrial cases. For the first case, several alternative realistic routes towards the UK are analysed and the optimal route minimising total carbon emissions is identified and tested in real conditions. For the second case, emissions towards several destinations are calculated and two alternative routes to southern Europe are compared, using several transport modes (road, Ro-Ro, rail and maritime). An adapted Value Stream Mapping (VSM) approach is used to map carbon footprint and calculate emissions; in addition Automatic Identification Systems (AIS) data provided information for vessel specification allowing the use of more accurate emission factors for each shipping leg. Findings – The analysis of the first case demonstrates that end-to-end logistics-related carbon emissions can be reduced by 16-21 per cent through direct delivery to the UK as opposed to transhipment via a Continental European port. The analysis of the second case shows that deliveries to southern Europe have the highest potential for reduction through deliveries by sea. Both cases show that for distant overseas destinations, the maritime leg represents the major contributor to CO2 emissions in the end-to-end supply chain. It is notable that one of the main apportionment approaches (that of Defra in the UK) generate higher carbon footprints for routes using Ro-Pax vessels, making those not optimal. The feasibility of the optimal route was demonstrated with real-life data. Originality/value – This research used real-life data from two UK companies and highlighted where carbon emissions are generated in the inbound and outbound transport chain, and how these can be reduced.