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Model of the Adaptive Information System on a Navigational Bridge

Published online by Cambridge University Press:  10 May 2016

Lovro Maglić ,
Damir Zec  and
Vlado Frančić
Lovro Maglić*
Affiliation:
(Faculty of Maritime Studies Rijeka, University of Rijeka, Croatia)
Damir Zec
Affiliation:
(Faculty of Maritime Studies Rijeka, University of Rijeka, Croatia)
Vlado Frančić
Affiliation:
(Faculty of Maritime Studies Rijeka, University of Rijeka, Croatia)
*
(E-mail: [email protected])
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Abstract

Adaptive Information Systems (AdIS) are systems responsive to environmental changes or changes in a ship's systems. In this paper the potential of shipboard AdIS to decrease an officer's excessive workload are examined. The workload of the Officer Of the Watch (OOW) consists of tasks being initiated by the OOW and by external inputs. Sometimes the external inputs, particularly those requiring low priority actions, actually distract the OOW and increase the workload. Consequently an overload may be reduced by delaying low priority information, thus delaying the actions they could initiate. To estimate the applicability of AdIS, a model has been developed using a discrete event simulation software, consisting of three main modules: environment, AdIS and the OOW. The simulation has been run with a traffic environment comparable to those existing in the Dover Strait. A comparison between the OOW workload with and without AdIS has been estimated, indicating that during demanding navigation AdIS can significantly reduce the overload time. In areas similar to the Dover Strait the overload time can be reduced by a third.

Keywords

AdaptationHuman elementWorkloadMan Machine Interface (MMI)Discrete event simulation
Type
Research Article
Information
The Journal of Navigation , Volume 69 , Issue 6 , November 2016 , pp. 1247 - 1260
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Beringer, D. (2002). Applying Performance-Controlled Systems, Fuzzy Logic, and FlyBy-Wire Controls to General Aviation. Final Report, Civil Aerospace Medical Institute, FAA, Oklahoma City.Google Scholar
Crowch, T. (2013). Navigating the Human Element. Kent: MLB Publishing.Google Scholar
Dornheim, M.A. (1999). Apache tests power of new cockpit tool. Aviation Week and Space Technology, 151(16), 46–49.Google Scholar
Embrey, D. (2006). Development of a Human Cognitive Workload Assessment Tool. Human Reliability Associates, Dalton Lancashire, MCA Final Report.Google Scholar
Gerdes, R. (2009). Reducing Risk in the English Channel/La Manche Traffic Separation Schemes. BMT Isis.Google Scholar
International Maritime Organization (IMO). (1974). SOLAS, International Convention for the Safety of Life at Sea, London: International Maritime Organization.Google Scholar
International Maritime Organization (IMO). (2001). Guidance on fatigue mitigation and management. MSC/Circ. 1014, London: International Maritime Organization.Google Scholar
International Maritime Organization (IMO). (2009). Code on alerts and indicators. Resolution A.1021(26). London: International Maritime Organization.Google Scholar
International Maritime Organization (IMO). (2010a). Guidelines for bridge equipment and systems, their arrangement and integration (BES). SN.1/Circ.288. London: International Maritime Organization.Google Scholar
International Maritime Organization (IMO). (2010b). Adoption of performance standards for bridge alert management (BAM). Resolution MSC.302(87). London: International Maritime Organization.Google Scholar
Kaber, D.B., Perry, C.M., Segall, N., McClernon, C.K. and Prinzel, L.J. (2006). Situation awareness implications of adaptive automation for information processing in an air traffic control-related task. International Journal of Industrial Ergonomics, 36(5), 447–462.CrossRefGoogle Scholar
Kum, S., Furusho, M., Duru, O. and Satir, T. (2007). Mental Workload of the VTS Operators by Utilising Heart Rate. TransNav, International Journal on Marine Navigation and Safety of Sea Transportation, 1(2), 145–151.Google Scholar
Letsu-Dake, E. and Ntuen, C.A. (2010). A case study of experimental evaluation of adaptive interfaces. International Journal of Industrial Ergonomics, 40(1), 34–40.CrossRefGoogle Scholar
Nachreiner, F., Nickel, P. and Meyer, I. (2006). Human factors in process control systems: The design of human–machine interfaces. Safety Science, 44(1), 5–26.CrossRefGoogle Scholar
NASA Langley Research Centre and Honeywell International Inc. (2013). Verification of Adaptive Systems. Final Report.Google Scholar
Nasoz, F., Lisetti, C.L. and Vasilakos, A.V. (2010). Affectively intelligent and adaptive car interfaces. Information Sciences, 180(20), 3817–3836.CrossRefGoogle Scholar
Piechulla, W., Mayser, C., Gehrke, H. and König, W. (2003). Reducing drivers’ mental workload by means of an adaptive man–machine interface. Transportation Research Part F: Traffic Psychology and Behaviour, 6(4), 233–248.CrossRefGoogle Scholar
Rouse, W.B. (1994). Twenty Years of Adaptive Aiding: Origins of the Concept and Lessons Learned. In Parasuraman R., Mouloua M., Human performance in automated systems: Current research and trends, New Jersey.Google Scholar
Steinhauser, N.B., Pavlas, D. and Hancock, P.A. (2009). Design Principles for Adaptive Automation and Aiding. Ergonomics in Design: The Quarterly of Human Factors Applications, 17(2), 6–10.CrossRefGoogle Scholar
Tzannatos, E.S. (2004). GMDSS False Alerts: A Persistent Problem for the Safety of Navigation at Sea. Journal of Navigation, 57(1), 153–159.CrossRefGoogle Scholar