Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Visual Analytics on Maritime Traffic Accidents in Tianjin Port
Chen Qinyu1,2, Xiao Jinli*1,2,Hu Bin3,Liu Jingsi 4 ,Chen Qinyang 1,2
1. Navigation School of Wuhan University of Technology,Wuhan, Hubei Province, China,;
2.Hubei Inland Shipping Technology Key Laboratory, Wuhan, Hubei Province, China,
3. Dongjiakou Maritime Safety Administration of the People's Republic of China, Qingdao, Shandong Province, China
4. Changjiang Waterway Bureau Survey Center, Wuhan, Hubei Province, China

ABSTRACT
Maritime accidents happen from time to time because of the severe
water traffic conditions, posing severe threats to the safety and securityof human life, property, and environmental protection, etc. It is of great
importance to carry out comprehensive researches on maritime accidents,
especially for guaranteeing safe navigation and improving transport
efficiency. Taking the maritime accidents in Tianjin Port areas from2005 to 2020 as the research object, the results of different graycorrelation theories are compared and analyzed, and a gray correlation
analysis method that is more consistent with the qualitative analysis isgiven to improve the analysis of water traffic accidents. Accuracy. For
random sequences that do not meet the requirements of gray systemmodeling, a practical sequence buffer operator is introduced, and themean and weighted GM (1, 1) prediction models are constructed
respectively to deeply explore the characteristics of the temporal andspatial distribution of water traffic accidents. The above methods canprovide solid theoretical support for the relevant departments of Tianjin
Port to formulate accident emergency plans and rationally allocate
emergency assistance resources.

KEY WORDS: maritime traffic accidents; grey relational analysis;
interactive visualization;spatial and temporal distribution characteristics

INTRODUCTION
Due to the irresistible trend of economic globalization, international
trade is becoming more and more prosperous. Water transportation has
become one of the most popular modes of transportation due to its
advantages of large volume and low freight (Kaluza et al. 2010). The
relevant statistics of the International Maritime Organization (IMO)
show that nearly 90% of the international trade in goods is completed bywater transportation (Zhiwei 2017). A large number of ships have been
put into waterway transportation to meet the urgent needs of society forwaterway transportation. However, due to the increase in the number ofships, the water navigation environment has become increasingly
complex, and water traffic accidents have occurred from time totime(Zhe et al. 2010). In order to improve transportation efficiency andensure the safety of water transportation, relevant departments haveformulated relevant rules and regulations, and actively promoted theapplication of relevant navigation aids.
The purpose of research on water traffic accidents is to explore themechanism of accident causative factors and provide theoretical support
for enhancing the safety of water transportation. Zhang et al. (2019)analyzed the ship accident investigation report and used non-parametricstatistical methods to quantitatively analyze the causal factors of theaccident, and found that ship speed is the main factor of casualties incollision accidents. Based on 894 marine accident data, Xingwei et al.
(2019) constructed a structural equation model to quantitatively analyze
the influencing factors of the severity of water traffic accidents.
Dongxing et al. (2019), Qinglong et al. (2013) used a weighted grey
system to analyze the degree of correlation between the types of water
traffic accidents and the total number of accidents. Jiawei et al. (2016)
explored the relationship between the total number of accidents andaccident types, and also deeply analyzed the time relationship between
the total number of accidents and the month; Xiuwu and Jian (2010)
Statistical analysis was carried out on the water traffic accidents in thewaters of the port, and the reasons for the occurrence of water traffic
accidents were systematically analyzed from four aspects: people, ships,environment and management, and corresponding safety management
countermeasures were put forward. Dangli et al. (2019) combined theGMG (1,n) model and the SARIMA model to predict the accidents in theTianjin waters based on the combination of the IOWA operator, andobtained the trend of accidents in the waters in the past three years.
As an emerging technical means, data visualization has been widely usedin many fields with the advantages of human-computer interaction. Datavisualization is mainly divided into scientific visualization, information
visualization and visual analysis(Ware 2012). Visualization technology
has achieved phased results in the field of water transportation(Jinyu etal. 2016, L et al. 1999, Scheepens et al. 2011, Willems et al. 2009).
systematically analyzed water traffic visualization from three aspects:
trajectory visualization, density visualization and attribute visualization,and summarized the challenges and future development trends of water
traffic visualization(Wuxiong et al. 2015).

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