Pre-warning system of maritime traffic safety risk in restricted visibility weather

DAI Hou-xing; WU Zhao-lin

doi:10.19818/j.cnki.1671-1637.2018.05.019

Volume 18 Issue 5

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Article Navigation > Journal of Traffic and Transportation Engineering > 2018 > 18(5): 195-206

DAI Hou-xing, WU Zhao-lin. Pre-warning system of maritime traffic safety risk in restricted visibility weather[J]. Journal of Traffic and Transportation Engineering, 2018, 18(5): 195-206. doi: 10.19818/j.cnki.1671-1637.2018.05.019

Citation:

DAI Hou-xing, WU Zhao-lin. Pre-warning system of maritime traffic safety risk in restricted visibility weather[J]. Journal of Traffic and Transportation Engineering, 2018, 18(5): 195-206. doi: 10.19818/j.cnki.1671-1637.2018.05.019

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Pre-warning system of maritime traffic safety risk in restricted visibility weather

doi: 10.19818/j.cnki.1671-1637.2018.05.019

DAI Hou-xing^{1, 2
,},
WU Zhao-lin^1
,

1.
School of Navigation, Dalian Maritime University, Dalian 116026, Liaoling, China
2.
China Yantai Salvage, Shandong, China

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Author Bio:
DAI Hou-xing(1966-), male, senior engineer, doctoral student, capt.dai@163.com

WU Zhao-lin(1947-), male, professor, wuzl2010@126.com
Received Date: 2018-06-10
Publish Date: 2018-10-25

Abstract

Abstract

To enhance the pre-warning applicability and accuracy of maritime traffic safety risk, a pre-warning system in restricted visibility weather of the risk was set up, and it was composed of the risk matrix knowledge base, traffic flow density prediction subsystem and visibility warning subsystem.By collecting large samples, the expert survey method was modified by using the fuzzy information distribution theory under the condition of incomplete information, and the maritime traffic risk matrix was determined.The traffic density was calculated by using the short-time prediction algorithm of traffic density based on the limit learning machine theory in the artificial neural network.The regional atmospheric model system was used to divide the visibility forecast data provided by the meteorological and marine forecasting departments into spatialtemporal fine meshes, and the visible distance was calculated.The system was used to predict the visibility distance and traffic flow density of the focused sea area with spatial grids of 2 nmile by2 nmile and time step of 10 min, so as to verify the effectiveness of the system.Simulation resultshows that at 12 time points in two different time periods, the prediction accuracy rates of visible distance are 75%, 75%, 80%, 75%, 80%, 75%, 75%, 75%, 80%, 80%, 80%and 75%.The prediction accuracy rates of corresponding traffic flow densities are up to 80%.Therefore, the forecast result is reliable, and the system can realize the visualization and intelligent monitoring of navigation risk in sea area in restricted visibility weather.

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References(31)

References

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Pre-warning system of maritime traffic safety risk in restricted visibility weather

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