The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation
Abstract
:1. Introduction
2. Research Framework of Automatic Identification Systems (AIS)
2.1. Technical Specifications and Algorithms
- Static, i.e., entered into the system on installation: Maritime Mobile Service Identity (MMSI), call sign and name, IMO number, length and beam, a type of ship and, location of electronic position fixing system (EPFS) antenna.
- Dynamic, i.e., navigational status and data from ship sensors: ship’s position with accuracy indication and integrity status, position timestamp in coordinated universal time (UTC), course over ground (COG), speed over ground (SOG), heading, navigational status, and rate of turn (ROT).
- Voyage-related (manually entered and updated during the voyage): ship’s draught, hazardous cargo (type: dangerous goods/harmful substances/marine pollutants), destination and estimated time of arrival (ETA), and route plan (waypoints).
- Safety-related: free format, short text messages that can be manually entered, addressed either a specific addressee or broadcast to all ships and shore stations.
2.2. Policy Regulation and Governance
- IMO Maritime Safety Committee (MSC), Resolution MSC.74(69): Recommendation on Performance Standards for Universal AIS;
- IMO Assembly (A), Resolution A.1106(29): Revised guidelines for the onboard operational use of shipborne AIS;
- IMO Maritime Safety Committee, Resolution MSC.347(91): Recommends that administrations should take the steps necessary to ensure the integrity of the radio channels used for AIS in their waters;
- IMO Maritime Safety Committee, Marine Safety Circular 1252: Guidelines on annual testing of AIS;
- IMO Maritime Safety Committee, Marine Safety Circular 1473: Policy on use of AIS aids to navigation (AIS AtoN);
- IMO Maritime Safety Committee, Safety of Navigation Circular 227: Guidelines for the installation of a shipborne AIS;
- IMO Maritime Safety Committee, Safety of Navigation Circular 244: Guidance on the use of the UN/LOCODE in the destination field in AIS messages;
- IMO Safety Maritime Committee, Safety of Navigation Circular 243/Rev.1: Guidelines for the presentation of navigational-related symbols, terms, and abbreviations;
- IMO Safety Maritime Committee, Safety of Navigation Circular 289: Guidance on the use of AIS application-specific messages;
- IMO Safety Maritime Committee, Safety of Navigation Circular 290: Guidance for the presentation and display of AIS application-specific messages information.
- The White House Cross Agency priority goal for leveraging data as a strategic asset [10];
- Executive Order 13480: Ocean policy to advance the economic, security, and environmental interests of the United States, and its efforts to publicly release maritime data [10];
- The Geospatial Data Act of 2018 [11];
- The Foundations for Evidence-Based Policymaking Act [11].
2.3. The Pathway of the AIS Data
3. A View of Data Science and Informatics
3.1. Collecting
3.2. Cleaning
3.3. Storing
3.4. Visualizing
3.5. Distributing
4. Review of Algorithms and Applications
4.1. Waterways Management
4.2. Safety and Security
4.2.1. Safety and Accident Prevention Applications
4.2.2. Security
4.3. Marine and Environmental Planning
4.4. Natural Resources Management
4.5. Freight Management and Economics
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- IMO. Focus on IMO: The New STCW Convention; International Maritime Organization: London, UK, 1997. [Google Scholar]
- IMO. Adoption of the Final Act and Any Instruments, Resolutions and Recommendations; Resulting from the Work of The Conference; Coast Guards: Washington, DC, USA, 2010. [Google Scholar]
- IMO. Regulations for Carriage of AIS. (I. M. Organization, Producer), AIS Transponders. 2019. Available online: http://www.imo.org/en/OurWork/Safety/Navigation/Pages/AIS.aspx (accessed on 2 August 2019).
- Gao, M.; Shi, G.Y. Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm. Sensors 2019, 19, 2706. [Google Scholar] [CrossRef] [PubMed]
- Mokashi, A.; Stewart, R.D. Expanding the Capabilities and Use of Automatic Identification Systems (AIS). In Logistics, Trade, and Transportation Symposium; Center for Logistics, Trade, and Transportation (CLTT): Gulfport, MS, USA, 2014. [Google Scholar]
- Marine Milltech. What is the Range for AIS Receivers? Available online: https://www.milltechmarine.com/faq.htm#a5 (accessed on 2 August 2019).
- Seybold, J.S. Introduction to RF Propagation; Wiley: Hoboken, NJ, USA, 2005; p. 330. [Google Scholar]
- Winder, S.; Davies, J.; Carr, J.J. Newnes Radio and RF Engineering Pocket Book; Newnes: Oxford, UK, 2002; Volume 3. [Google Scholar]
- U.S. Coast Guard. Maritime Commerce Strategic Outlook; Coast Guard: Washington, DC, USA, 2018. [Google Scholar]
- White House. Executive Order Regarding the Ocean Policy to Advance the Economic, Security, and Environmental Interests of the United States; Executive Orders: Washington, DC, USA, 2018. [Google Scholar]
- U.S. Congress. Geospatial Data Act of 2018 (GDA), (P.L. 115–245), H.R. 302, Subtitle F. Sections 751–759. Washington, DC, USA, 2018. Available online: https://www.fgdc.gov/gda/geospatial-data-act-of-2018.pdf (accessed on 2 August 2019).
- U.S. CMTS. Enhancing Accessibility and Usability of Automatic Identification Systems (AIS) Data: Across the Federal Government and for the Benefit of Public Stakeholders; U.S. Committee on the Marine Transportation System: Washington, DC, USA, 2019.
- Proença, D.; Borbinha, J. Maturity Models for Data and Information Management. Lect. Notes Comput. Sci. 2018, 11057, 81–93. [Google Scholar] [CrossRef]
- Albert, T. Measuring Technology Maturity: Theoretical Aspects; Springer Science and Business Media LLC: Berlin, Germany, 2016; pp. 9–113. [Google Scholar]
- Office for Coastal Management. Vessel Traffic Data; NOAA: Washington, DC, USA, 2018.
- Felski, A.; Jaskólski, K. The Integrity of Information Received by Means of AIS During Anti-collision Manoeuvring. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 2013, 7, 95–100. [Google Scholar] [CrossRef] [Green Version]
- Felski, A.; Jaskólski, K.; Banyś, P. Comprehensive Assessment of Automatic Identification System (AIS) Data Application to Anti-collision Manoeuvring. J. Navig. 2015, 68, 697–717. [Google Scholar] [CrossRef] [Green Version]
- Felski, A.; Jaskólski, K. Information Unfitness of AIS. Annu. Navig. 2012, 19, 17–24. [Google Scholar] [CrossRef]
- Hori, A.; Arai, Y.; Okuda, S. Study on Application of Real Time AIS Information. In Proceedings of the Asia Navigation Conference, Jeju, Korea, 20–21 October 2006; Korean Institute of Navigation and Port Research: Busan, Korea, 2006; pp. 63–70. [Google Scholar]
- MarineCadastre.gov. Tutorial: How to Build Vessel Density Maps with AIS; Bureau of Energy Management & NOAA: Washington, DC, USA, 2015.
- Willems, N.; Van Hage, W.R.; De Vries, G.; Janssens, J.H.; Malaisé, V. An integrated approach for visual analysis of a multisource moving objects knowledge base. Int. J. Geogr. Inf. Sci. 2010, 24, 1543–1558. [Google Scholar] [CrossRef]
- NOAA. Tutorial: How to Build Vessel Density Maps with AIS; NOAA: Washington, DC, USA, 2015.
- U.S. Army Corps of Engineers. Automatic Identification System Analysis Package; U.S. Army Corps of Engineers: Washington, DC, USA, 2017.
- Mascaro, S.; Nicholso, A.E.; Korb, K.B. Anomaly detection in vessel tracks using Bayesian networks. Int. J. Approx. Reason. 2014, 55, 84–98. [Google Scholar] [CrossRef]
- Touzinsky, K.F.; Scully, B.M.; Mitchell, K.N.; Kress, M.M. Using Empirical Data to Quantify Port Resilience: Hurricane Matthew and the Southeastern Seaboard. J. Waterw. Port Coast. Ocean Eng. 2018, 144. [Google Scholar] [CrossRef]
- Li, M.X.; Mou, J.; Liu, R.; Chen, P.F.; Dong, Z.J.; He, Y.X. Relational Model of Accidents and Vessel Traffic Using AIS Data and GIS: A Case Study of the Western Port of Shenzhen City. J. Mar. Sci. Eng. 2019, 7, 163. [Google Scholar] [CrossRef]
- Kanarik, H.; Tuomi, L.; Alenius, P.; Lensu, M.; Miettunen, E.; Hietala, R. Evaluating Strong Currents at a Fairway in the Finnish Archipelago Sea. J. Mar. Sci. Eng. 2018, 6, 122. [Google Scholar] [CrossRef]
- National Marine Electronics Association. NMEA 0183-Standard for Interfacing Marine Electronic Devices. Standard for Interfacing; National Marine Electronics Association: Severna Park, MD, USA, 2002. [Google Scholar]
- Actisense. The NMEA 0183 Information Sheet; Active Research Limited: Dorset, UK, 2015. [Google Scholar]
- U.S. Congress. Maritime Transportation Security Act of 2002, Public Law 107-295, 46 USC 2101. In Proceedings of the 107th Congress of the United States of America, Washington, DC, USA, 25 November 2002. [Google Scholar]
- Pelich, R.; Chini, M.; Hostache, R.; Matgen, P.; Lopez-Martinez, C.; Nuevo, M.; Eiden, G. Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentine-1 and AIS Data. Remote Sens. 2019, 11, 1078. [Google Scholar] [CrossRef]
- Perez, H.M.; Chang, R.; Billings, R.; Kosub, T. Automatic Identification Systems (AIS) Data Use in Marine Vessel Emission Estimation. In Proceedings of the 19th Annual International Emission Inventory Conference, Baltimore, MA, USA, 27–30 September 2009; pp. 1–21. [Google Scholar]
- UNCTAD. Review of Maritime Transport 2017. New York and Geneva, 2017; United Nations Conference on Trade and Development: Geneva, Switzerland, 2017. [Google Scholar]
- European Union. How to Decarbonise European Transport by 2050; European Federation for Transport and Environment AISBL: Brussels, Belgium, 2018. [Google Scholar]
- Saputra, H.; Muvariza, M.F.; Satoto, S.W.; Koto, J. Estimation of Exhaust Ship Emission from Marine Traffic in the Straits of Singapore and Batam Waterways Using Automatic Identification System (AIS). Data J. Teknol. 2015, 77, 47–53. [Google Scholar]
- IMO. Third IMO Greenhouse Gas Study 2014; International Maritime Organization: London, UK, 2015. [Google Scholar]
- Kurekin, A.A.; Loveday, B.R.; Clements, O.; Quartly, G.D.; Miller, P.I.; Wiafe, G.; Agyekum, K.A. Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation and AIS Data. Remote. Sens. 2019, 11, 293. [Google Scholar] [CrossRef]
- Snapir, B.; Waine, T.W.; Biermann, L. Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea. Remote Sens. 2016, 33, 353. [Google Scholar] [CrossRef]
- Xiu, S.; Wen, Y.; Yuan, H.; Xiao, C.; Zhan, W.; Zou, X.; Zhou, C.; Shah, S.C. A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification. Sensors 2019, 19, 1317. [Google Scholar] [CrossRef] [PubMed]
- U.S. Army Corps of Engineers. HarborSym: A Data-Driven Monte Carlo Simulation Model of Vessel Movement in Harbors; U.S. Army Corps of Engineers: Washington, DC, USA, 2004.
- U.S. Army Corps of Engineers. HarborSym Application User’s Manual; Institute for Water Resources; Container Model Suite: Washington, DC, USA, 2013.
- Vanek, O.; Jakob, M.; Hrstka, O.; Pechoucek, M. Using Multi-Agent Simulation to Improve the Security of Maritime Transit. Multi-Agent-Based Simulation XII. 7124; Villatoro, D., Sabater-Mir, J., Sichman, J., Eds.; Cyber Security Center: Berlin/Heidelberg, Germany, 2011; pp. 44–58. [Google Scholar] [CrossRef]
- Olmer, N.; Comer, B.; Roy, B.; Mao, X.; Rutherford, D. Greenhouse Gas Emissions from Global Shipping, 2013–2015; The International Council on Clean Transportation: Washington, DC, USA, 2017. [Google Scholar]
- Psaraftis, H.N. Speed Optimization vs Speed Reduction: The Choice between Speed Limits and a Bunker Levy. Sustainability 2019, 11, 2249. [Google Scholar] [CrossRef]
- Alessandrini, A.; Guizzardi, D.; Janssens-Maenhout, G.; Pisoni, E.; Trombetti, M.; Vespe, M. Estimation of shipping emissions using vessel Long Range Identification and Tracking data. J. Maps 2017, 13, 946–954. [Google Scholar] [CrossRef]
- Dominioni, G.; Heine, D.; Romera, B.M. Regional Carbon Pricing for International Maritime Transport; World Bank Group: Washington, DC, USA, 2018. [Google Scholar]
- Nikolic, D.; Stojkovic, N.; Popovic, Z.; Tosic, N.; Lekic, N.; Stankovic, Z.; Doncov, N. Maritime Over the Horizon Sensor Integration: HFSWR Data Fusion Algorithm. Remote. Sens. 2019, 11, 852. [Google Scholar] [CrossRef]
- Santora, M.; Novak, B. Before Fatal Boat Crash in Budapest, Safety Warnings Went Unheeded; The New York Times: New York, NY, USA, 2019. [Google Scholar]
Federal Agency | Waterways Management | Waterways Safety and Security | Marine and Environmental Planning | Natural Resources Management | Freight Management and Economy |
---|---|---|---|---|---|
BOEM | Identify the historic patters and usage of offshore areas for safety and risk analysis | Inform site decisions, emission inventories Evaluate development plan | Support offshore energy development | ||
BTS | High-resolution of vessel traffic | Timely statistics on port and terminal usage Analyzing dwell times | |||
Environment Protection Agency (EPA) | Animal protection Pollution protection Estimate exhaust emission | Wind Energy | Emission estimation | ||
MARAD | Visualize the locations and routes of vessels for security | Fisheries | Port management Vessel efficiency | ||
NOAA | Monitoring water ways for emergency | Environmental protection | Fisheries & energy management | ||
The Saint Lawrence Seaway Development Corporation (SLSDC) | Track the position and course of commercial maritime traffic | Monitor the speed of commercial maritime vessel | |||
USACE | Monitor waterway | Assist communication of safety information | Channel management | Monitor lock performance | |
USCG | Vessel traffic monitoring | Collision avoidance | Assist water planning |
Categories | General User | Power User |
---|---|---|
Ease of Use | Interested in standard analytical products (track line, vessel density map), less intimate with technical AIS data analysis functions | Advancements beyond the scope of recommendations and required skills need to be provided |
Reliability | Unable to validate various data sources. | Storage of data agencies. Various data agencies keep AIS data independently with varying levels of management. |
Informative | Limited AIS fields, providing only a period of less than one year, having a large time interval between data readings, and describing only geographically limited areas | USACE has a short-term, 45-day temporary archive for internal use of its AIS data. NAVCEN stores three years of AIS data in a format accessible through HDR. |
Affordability | Only limited information provided by government agencies; expensive commercial data | Purchasing AIS data from commercial data vendors to meet the needs of their agencies |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lee, E.; Mokashi, A.J.; Moon, S.Y.; Kim, G. The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation. J. Mar. Sci. Eng. 2019, 7, 287. https://doi.org/10.3390/jmse7090287
Lee E, Mokashi AJ, Moon SY, Kim G. The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation. Journal of Marine Science and Engineering. 2019; 7(9):287. https://doi.org/10.3390/jmse7090287
Chicago/Turabian StyleLee, EunSu, Amit J. Mokashi, Sang Young Moon, and GeunSub Kim. 2019. "The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation" Journal of Marine Science and Engineering 7, no. 9: 287. https://doi.org/10.3390/jmse7090287
APA StyleLee, E., Mokashi, A. J., Moon, S. Y., & Kim, G. (2019). The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation. Journal of Marine Science and Engineering, 7(9), 287. https://doi.org/10.3390/jmse7090287