Vessel incidents periodically occur in the waterways of the United States, but some types of commercial vessels have shown a downward trend in the number of incidents in recent years. One of the missions of the United States Coast Guard (USCG) is to develop regulations to mitigate and potentially prevent maritime incidents. In this paper, the USCG gathered data on more than 117,000 incidents that involved U.S.-flag vessels in U.S. waterways for the period 2001 through 2018. We applied the Grey System theory or model and Grey-Markov forecasting model to predict the future number of vessel incidents for four different vessel types from 2019 through 2030. Incident data can vary considerably from year to year and often can be incomplete. The Grey-Markov model, which is a combination of the Grey model and the Markov chain process, is suitable for this purpose because of its predictive ability. From our results, we found that the Grey-Markov model performed exceptionally well and showed the predicted values of the number of incidents to be remarkably similar to the actual values with acceptable mean relative errors ranging from 5.2% to 8.2%. We expect that these results will benefit decision makers in formulating sound policies thereby improving the maritime safety of vessels operating in waterways of the United States.
Forecasting U.S. Maritime Incidents using the Grey-Markov Model
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2675 , 9 ; 1621-1630
2021-04-04
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Forecasting Trans-Pacific Air Traffic by Grey Model
British Library Conference Proceedings | 1999
|Grey forecasting model for active vibration control systems
Online Contents | 2009
|Motor Vehicles Forecasting in Kolhapur City Using Combined Grey Model
Springer Verlag | 2023
|A Hybrid Grey Markov Prediction Model for Traffic Volume
British Library Conference Proceedings | 2007
|A Hybrid Grey Markov Prediction Model for Traffic Volume
ASCE | 2007
|