Below is an overview of the AIS (Automatic Identification System), a maritime identification and tracking system used to enhance navigational safety and improve traffic management at sea. The information provided is general in nature and does not replace regulations and technical specifications issued by competent authorities (IMO, ITU, etc.).

1. Definition and Purpose

AIS (Automatic Identification System) is a communication and tracking system enabling ships to automatically and continuously broadcast and receive information on their position, course, speed, and other relevant data. These data are transmitted over VHF maritime frequencies (generally 161.975 MHz and 162.025 MHz) and can be received by:

• Other AIS-equipped vessels.
• Coastal stations (Vessel Traffic Services – VTS).
• Port authorities and control agencies.

The primary goal of AIS is to reduce collision risk and support traffic control and management in maritime areas. AIS data supplement radar, ECDIS (Electronic Chart Display and Information System), and other navigational devices.

2. How AIS Works

1. GPS and Timing

   • Each AIS unit incorporates a GPS receiver that provides accurate position, course, and speed.
   • GPS also supplies the timing necessary for AIS transmissions to synchronize in shared “time slots.”

2. VHF Transmission and Reception

   • An AIS unit automatically transmits its data via a dedicated low-power VHF transmitter.
   • At the same time, it receives AIS broadcasts from other units within radio range.

3. Dynamic Time Slots (TDMA)

   • AIS uses a TDMA (Time Division Multiple Access) protocol, dividing time into slots assigned to each vessel dynamically, based on traffic density.
   • This approach minimizes radio frequency collisions and ensures efficient data exchange even in busy shipping areas.

4. Processing and Display

   • Data from other ships are processed and shown on a dedicated AIS display or integrated into an ECDIS or similar system, allowing crews to see real-time positions and movements of nearby AIS-equipped vessels.

3. Types of AIS Equipment

1. AIS Class A

   • Mandatory for vessels subject to SOLAS (Safety of Life at Sea) regulations, such as passenger ships and larger commercial vessels above certain tonnage thresholds.
   • Transmits at higher power and with more frequent position updates than Class B.

2. AIS Class B

   • Intended for leisure craft and smaller vessels not covered by SOLAS.
   • Operates at lower power and with lower transmission priority, but still enables transmission and reception of AIS data.

3. AIS-SART (Search and Rescue Transmitter)

   • A portable or fixed device used aboard survival craft (e.g., life rafts), broadcasting an emergency AIS signal to facilitate faster localization and rescue.

4. Base Station AIS

   • Shore-based stations operated by authorities or port agencies for monitoring purposes, often integrated with VTS (Vessel Traffic Service) for comprehensive traffic management.

4. International Regulations and Standards

• IMO (International Maritime Organization): Through SOLAS (Chapter V, Regulation 19) and subsequent amendments, the IMO has introduced mandatory AIS use on passenger vessels and larger merchant ships.
• ITU (International Telecommunication Union): Defines AIS technical specifications, including the designated VHF channels.
• IEC (International Electrotechnical Commission): Publishes testing and certification standards for AIS equipment (e.g., IEC 61993-2).

5. Benefits and Advantages

1. Collision Prevention

   • Enhances situational awareness by providing detailed information about other vessels’ position, identity, and navigation status.

2. Navigational Safety

   • Supports coordination with coastal traffic services (VTS), especially in dense traffic areas or busy ports.
   • Facilitates rescue operations by helping authorities pinpoint a vessel in distress.

3. Maritime Traffic Management

   • Enables coastal stations and port authorities to monitor and optimize vessel movements, port entries/exits, and maneuvers in real time.
   • Assists in statistical analyses, route planning, toll systems, and reporting.

4. Additional Services

   • Capable of transmitting text messages and safety advisories (AIS Messages), such as weather warnings or navigation hazard alerts.

6. Limitations and Challenges

1. Reliance on Transponder

   • Vessels without AIS (or with AIS turned off) remain invisible to AIS, requiring radar or other “see-and-avoid” methods.

2. Channel Saturation in High-Traffic Areas

   • In busy ports and shipping lanes, the available time slots may become congested, causing delays or dropped transmissions.

3. GPS Dependence

   • If GPS signals are interrupted or malfunctioning, AIS data accuracy can degrade significantly.

4. Cybersecurity

   • AIS transmissions can potentially be spoofed or falsified by malicious actors, posing risks for maritime security.

7. Conclusion

The AIS system is a key tool for improving maritime safety and efficiency. By continuously and automatically sharing vessel data (position, course, speed, identity), AIS complements traditional means of detection such as radar and visual lookout, offering substantial benefits in terms of:

• Reducing collision risk.
• Enhancing situational awareness among ships and shore stations.
• Optimizing traffic flow in busy waterways and commercial routes.

Nonetheless, proper installation, maintenance, and monitoring are crucial to mitigate issues such as channel congestion, tampering, or improper use. Adherence to international standards (IMO, ITU, IEC) and ongoing updates to regulations help maximize the effectiveness of AIS, establishing it as a cornerstone of modern maritime safety.

References__________________________________________________________________________

Hermannsen L, Mikkelsen L, Tougaard J, Beedholm K, Johnson M, Madsen PT. Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape. Sci Rep. 2019 Oct 29;9(1):15477. doi: 10.1038/s41598-019-51222-9. 

Abstract. Recreational boating is an increasing activity in coastal areas and its spatiotemporal overlap with key habitats of marine species pose a risk for negative noise impacts. Yet, recreational vessels are currently unaccounted for in vessel noise models using Automatic Identification System (AIS) data. Here we conduct a case study investigating noise contributions from vessels with and without AIS (non-AIS) in a shallow coastal area within the Inner Danish waters. By tracking vessels with theodolite and AIS, while recording ambient noise levels, we find that non-AIS vessels have a higher occurrence (83%) than AIS vessels, and that motorised recreational vessels can elevate third-octave band noise centred at 0.125, 2 and 16 kHz by 47-51 dB. Accordingly, these vessels dominated the soundscape in the study site due to their high numbers, high speeds and proximity to the coast. Furthermore, recreational vessels caused 49-85% of noise events potentially eliciting behavioural responses in harbour porpoises (AIS vessels caused 5-24%). We therefore conclude that AIS data would poorly predict vessel noise pollution and its impacts in this and other similar marine environments. We suggest to improve vessel noise models and impact assessments by requiring that faster and more powerful recreational vessels carry AIS-transmitters.

Natale, F., Gibin, M., Alessandrini, A., Vespe, M., & Paulrud, A. (2015). Mapping fishing effort through AIS data. PloS one, 10(6), e0130746.

Abstract. Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels’ speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers.

Xu, H., Rong, H., & Soares, C. G. (2019). Use of AIS data for guidance and control of path-following autonomous vessels. Ocean Engineering, 194, 106635.

Abstract. This paper carries out research on the prototype of an autonomous vessel that is normally used for passenger transportation. A path generation system is proposed using a dynamic time warping algorithm based on Automatic Identification System history data. In order to ensure strong stability properties, a nonlinear controller with globally exponentially stability is introduced for the heading controller. The least square support vector machine is employed to estimate the parameters of the control model based on full-scale manoeuvring tests. The identified results are validated with analytical values. The kinematics and control object of path-following are presented, and a time-varying vector field guidance law is proposed for the path-following control of autonomous vessels. The guidance and control subsystems are interconnected and form a cascade structure. The stability of the coupled guidance and control system is carried out using the cascaded system theory. The proof ensures that the equilibrium point of the cascaded system is uniformly semiglobally exponentially stable. Path following scenario was considered in the presence of currents based on a nonlinear manoeuvring model. Simulation studies were used to verify the theoretical results.

Robards, M. D., Silber, G. K., Adams, J. D., Arroyo, J., Lorenzini, D., Schwehr, K., & Amos, J. (2016). Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review. Bulletin of Marine Science, 92(1), 75-103.

Abstract. The continued development of maritime transportation around the world, and increased recognition of the direct and indirect impacts of vessel activities to marine resources, has prompted interest in better understanding vessel operations and their effects on the environment. Such an understanding has been facilitated by Automatic Identification Systems (AIS), a mandatory vessel communication and navigational safety system that was adopted by the International Maritime Organization in 2000 for use in collision avoidance, coastal surveillance, and traffic management. AIS is an effective tool for accomplishing navigational safety goals, and by doing so, can provide critical pre-emptive maritime safety benefits, but also provides a data opportunity with which to understand and help mitigate the impacts of maritime traffic on the marine environment and wildlife. However, AIS was not designed with research or conservation planning in mind, leading to significant challenges in fully benefiting from use of the data for these purposes. We review present experiences using AIS data for strategic conservation applications, and then focus on efforts to ensure archived and real-time AIS data for key variables reflect the best available science (of known limitations and biases). We finish with a suite of recommendations for users of the data and for policy makers.

Hexeberg, S., Flåten, A. L., & Brekke, E. F. (2017, July). AIS-based vessel trajectory prediction. In 2017 20th international conference on information fusion (Fusion) (pp. 1-8). IEEE.

Abstract:. In order for autonomous surface vessels (ASVs) to avoid collisions at sea it is necessary to predict the future trajectories of surrounding vessels. This paper investigate the use of historical automatic identification system (AIS) data to predict such trajectories. The availability of AIS data have steadily increased in the last years as a result of more regulations, together with wider coverage through AIS integration on satellites and more land based receivers. Several AIS-based methods for predicting vessel trajectories already exist. However, these prediction techniques tend to focus on time horizons in the level of hours. The prediction time of our interest typically ranges from a few minutes up to about 15 minutes, depending on the maneuverability of the ASV. This paper presents a novel datadriven approach which recursively use historical AIS data in the neighborhood of a predicted position to predict next position and time. Three course and speed prediction methods are compared for one time step predictions. Lastly, the algorithm is briefly tested for multiple time steps in curved environments and shows good potential.

Svanberg, M., Santén, V., Hörteborn, A., Holm, H., & Finnsgård, C. (2019). AIS in maritime research. Marine Policy, 106, 103520.

Abstract. Although not originally developed for research use, the Automatic Identification System (AIS) enables its data to be used in research. The present paper provides a structured overview of how AIS data is used for various research applications. Ten areas have been identified, spread across maritime, marine and other journals. Many stakeholders beyond the most frequently mentioned – authorities and maritime administrations – can benefit from the research in which AIS data is used. AIS data can be incorporated in various types of modelling approaches and play a small or large role as a source of data. AIS data can also be validated or used to validate research from other data sources. Although a large amount of AIS-based research adds to the literature, there is still a large potential for using AIS data for research by making greater use of the variety in AIS messages, combining AIS with other sources of data, and extending both spatial and temporal perspectives.

Liu, R. W., Zhou, S., Yin, S., Shu, Y., & Liang, M. (2024). AIS-based vessel trajectory compression: a systematic review and software development. IEEE Open Journal of Vehicular Technology.

Abstract: With the advancement of satellite and 5G communication technologies, vehicles can transmit and exchange data from anywhere in the world. It has resulted in the generation of massive spatial trajectories, particularly from the Automatic Identification System (AIS) for surface vehicles. The massive AIS data lead to high storage requirements and computing costs, as well as low data transmission efficiency. These challenges highlight the critical importance of vessel trajectory compression for surface vehicles. However, the complexity and diversity of vessel trajectories and behaviors make trajectory compression imperative and challenging in maritime applications. Therefore, trajectory compression has been one of the hot spots in research on trajectory data mining. The major purpose of this work is to provide a comprehensive reference source for beginners involved in vessel trajectory compression. The current trajectory compression methods could be broadly divided into two types, batch (offline) and online modes. The principles and pseudo-codes of these methods will be provided and discussed in detail. In addition, compressive experiments on several publicly available data sets have been implemented to evaluate the batch and online compression methods in terms of computation time, compression ratio, trajectory similarity, and trajectory length loss rate. Finally, we develop a flexible and open software, called AISCompress , for AIS-based batch and online vessel trajectory compression. The conclusions and associated future works are also given to inspire future applications in vessel trajectory compression.