Il Sistema AIS (Automatic Identification System) è un sistema di identificazione automatica utilizzato in ambito marittimo per aumentare la sicurezza della navigazione e migliorare la gestione del traffico navale. Le informazioni fornite hanno carattere generale e non sostituiscono le norme e le specifiche tecniche emanate dalle autorità competenti (IMO, ITU, ecc.).

1. Definizione e Obiettivi

L’AIS (Automatic Identification System) è un sistema di comunicazione e monitoraggio che consente alle navi di trasmettere e ricevere, in modo automatico e continuo, informazioni sulla propria posizione, rotta, velocità e altri dati rilevanti. Tali informazioni vengono condivise via radio VHF marittima (in genere sulle frequenze dedicate 161,975 MHz e 162,025 MHz) e possono essere recepite da:

• Altre unità navali dotate di AIS.
• Stazioni costiere (VTS – Vessel Traffic Service).
• Enti di controllo e autorità portuali.

L’obiettivo primario dell’AIS è ridurre i rischi di collisione e fornire supporto alle attività di controllo e gestione del traffico marittimo, integrandosi con radar, ECDIS (Electronic Chart Display and Information System) e altri dispositivi di navigazione.

2. Principio di Funzionamento

1. GPS e temporizzazione

   • Ogni unità AIS integra un ricevitore GPS per determinare con precisione la propria posizione, rotta e velocità.
   • Il GPS fornisce anche la temporizzazione necessaria per sincronizzare le trasmissioni AIS in “time slots” condivisi.

2. Trasmissione e ricezione su VHF

   • L’unità AIS trasmette automaticamente i propri dati tramite un apposito trasmettitore VHF a bassa potenza.
   • Al tempo stesso, riceve i dati AIS inviati dalle altre unità nel raggio di copertura radio.

3. Slot di invio dinamici (TDMA)

   • La tecnologia AIS adotta un protocollo TDMA (Time Division Multiple Access) che divide il tempo in intervalli (slot) assegnati dinamicamente a ciascuna unità in base al traffico presente.
   • Ciò minimizza le collisioni radio e garantisce una trasmissione efficiente delle informazioni anche in aree con elevata densità di traffico navale.

4. Elaborazione e visualizzazione

   • I dati ricevuti da ogni unità vengono elaborati e mostrati su un display dedicato o su un sistema integrato (es. ECDIS), consentendo a comandante e ufficiali di vedere in tempo reale la posizione e i movimenti delle altre navi dotate di AIS.

3. Tipologie di Apparati AIS

1. AIS Classe A

   • Obbligatorio per le navi soggette alle normative SOLAS (Safety of Life at Sea), come navi passeggeri e unità commerciali sopra una certa stazza.
   • Trasmette a potenza più elevata e con un aggiornamento dati più frequente rispetto alla Classe B.

2. AIS Classe B

   • Destinato a unità da diporto e piccole imbarcazioni non soggette a obblighi SOLAS.
   • Opera a potenza ridotta e con minor priorità nell’accesso agli slot, ma garantisce comunque la trasmissione/ricezione delle informazioni AIS.

3. AIS-SART (Search And Rescue Transmitter)

   • Dispositivo portatile o fisso a bordo di mezzi di soccorso (es. zattere di salvataggio) che trasmette una segnalazione AIS di emergenza, agevolando la localizzazione e il recupero da parte dei soccorritori.

4. Base Station AIS

   • Stazioni costiere gestite dalle autorità di controllo o dalle società portuali, utilizzate per il monitoraggio e l’integrazione dei dati AIS con i sistemi di gestione del traffico (VTS).

4. Normative e Standard Internazionali

• IMO (International Maritime Organization): tramite il SOLAS (capitolo V, Regola 19) e successive modifiche ha introdotto e disciplinato l’uso dell’AIS a bordo delle navi passeggeri e mercantili.
• ITU (International Telecommunication Union): definisce le specifiche tecniche per le trasmissioni AIS (es. canali VHF dedicati).
• IEC (International Electrotechnical Commission): stabilisce le norme di collaudo e certificazione degli apparati AIS (es. IEC 61993-2).

5. Vantaggi e Benefici

1. Prevenzione delle collisioni

   • Migliora la consapevolezza situazionale, fornendo ai comandanti informazioni dettagliate su rotta, velocità, posizione e identità delle altre unità nelle vicinanze.

2. Sicurezza della navigazione

   • Supporta il coordinamento con le autorità costiere (VTS), specialmente in aree affollate o porti trafficati.
   • In caso di emergenza, agevola la localizzazione e il soccorso.

3. Gestione del traffico marittimo

   • Consente alle stazioni costiere di monitorare in tempo reale le rotte delle navi, ottimizzando ingressi, uscite e manovre in porto.
   • Supporta analisi statistiche, pianificazione delle rotte, sistemi di pedaggio e reporting.

4. Servizi addizionali

   • Possibilità di trasmettere messaggi di testo e avvisi di sicurezza (AIS Messages), come bollettini meteorologici o informazioni su condizioni di navigazione pericolose.

6. Limiti e Criticità

1. Dipendenza dal transponder

   • Le navi prive di AIS o con AIS disattivato risultano invisibili al sistema, mantenendo comunque la necessità di radar e altre tecniche di “see and avoid”.

2. Saturazione dei canali in aree congestionate

   • In porti e zone di intenso traffico, può verificarsi un sovraccarico dei time slots, con possibili ritardi o mancate trasmissioni.

3. Affidabilità del GPS

   • In caso di interruzioni o guasti al segnale GPS, la precisione dei dati AIS può risentirne.

4. Sicurezza informatica

   • In alcuni contesti è possibile la manipolazione o l’invio di informazioni AIS false o fuorvianti da parte di soggetti malintenzionati.

Bibliografia__________________________________________________________________________

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.