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

Analysis of the Spatiotemporal Distribution and Characteristics of Ships Accidents
in Yangtze River using Big Data for Emergency Needs
Quandang Maa,b, Huan Tanga, Fucai Jianga, Zhou Yanga *a School of Navigation, Wuhan University of Technologyb Intelligent Transportation Systems Center, Wuhan University of Technology
Wuhan, Hubei, China

ABSTRACTa corresponding emergency response plan to ensure the safety of inland
river traffic, and reduce economic losses. Huang, Hu and Li (2013)
explored the spatial distribution of maritime accidents around the world
and demonstrated that accident distribution has spatial characteristics.
Explore the spatial distribution characteristics of inland river accidents,
it is conducive to rational emergency resource allocation, and canenhance emergency response capacity. Wang and Zhixian (2020)studied the spatiotemporal characteristics of maritime accidents ininland rivers from the aspects of accident occurrence and accident
consequence. These studies are helpful to explore the spatiotemporal
distribution of inland river accidents. However, there is no furtherdiscussion on the impact of spatiotemporal distribution on emergency
resource allocation. Therefore, the paper analyzes the characteristics
and spatiotemporal distribution of accidents in Jiangsu section, and puts
forward the emergency needs for accidents.
In order to improve maritime traffic safety, many scholars aroundthe world have studied and analyzed maritime accidents. Zhang, Sun,
Chen and Cheng (2021) used Kernel Density Estimation(KDE) and Kmeans clustering to analyze the characteristics and spatiotemporal
distribution of global maritime accidents. Zhang,Manderbacka, Kujalaand Hirdaris (2021) used big data to analyze the traffic conditions andhydrometeorological conditions encountered by ships during specificnavigation, so as to facilitate ship collision avoidance. Ma, Jiang, Wangand Wang (2018) proposed to evaluate the risk of ship navigation
environment based on the improved TOPSIS method. Gonschorek(2020) proposed using KDE to analyze massive location data hotspots
visually. Wang, Cheng, Zheng and Wu (2020) used Automatic
Identification System(AIS) data to cluster Wuhan ship trajectories byDensity-Based Spatial Clustering of Applications with Noise(DBSCAN)
and calculate ship behavior patterns. Chen, Liu, Li, Ma, Sun and Ieee
(2017) proposed to use Non—dominated Sorting Genetic
Algorithm(NSGA) to locate emergency rescue sites and improve
emergency efficiency.
The paper is based on the maritime accidents data of Jiangsu
section from 2016 to 2018. (1) Preprocess the data and study the single
latitude characteristics of accidents; (2) KDE and DBSCAN clustering
were used to determine the accident-prone waters in the Jiangsu section,

Maritime accidents are one of the main reasons behind theinefficient emergency rescue. However, the existing emergency
response plans ignore the impact of accident characteristics onemergency rescue. Thus, to better understand the features of ships
accidents for emergency management in the inland river, the paper
introduces a big data analysis methodology to analyze thespatiotemporal distribution and characteristics of traffic accidents. And
the targeted emergency response plan is explored, combining with thecharacteristics of inland river traffic accidents. To demonstrate theapplicability of the method, detailed accident data from the 2016-2018
years of the Jiangsu section of Yangtze River were collected to conduct
big data analysis. The results demonstrate an improved understandingof the spatiotemporal distribution and detailed characteristics of inland
river traffic accidents that may in turn lead to better emergency
response plans. The identification of accident hotspots provides atargeted method to improve the level of inland river emergencymanagement.

KEY WORDS: Accident analytic; Emergency response; Big data;
Yangtze River .

INTRODUCTION
In recent years, economic globalization has rapidly promoted thedevelopment of the world economy. According to the statistical
Bulletin on the development of the transport industry in 2020. The
length of Chinese inland rivers increased by 387 kilometers comparedwith last year, with a net deadweight of 27,061,016 million tons, up 5.4
percent. However, with the development of the shipping industry, there
will be more traffic accidents. In 2020, there were 138 maritimeaccidents, 0.7% more than last year, and 196 deaths and missing
persons, an increase of 26.5% over last year. The Yangtze River is oneof the important inland rivers for inland and external exchanges inChina. Shipping safety has become an increasingly important issue.
The paper analyses the characteristics of inland river accidents, propose

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