Developing a Ship Collision Risk Assessment Model with Internal and External Factors: Focused on South Korea Maritime Environment

被引:0
作者
Park, Sangwon [1 ]
Yeo, Jiho [2 ]
Lee, Kyonghan [3 ]
机构
[1] Chonnam Natl Univ, Dept Maritime Police Sci, 50 Daehak Ro, Yeosu Si 59626, Jeollanam Do, South Korea
[2] Gachon Univ, Dept Smart City, 1342 Seongnam Daero, Seongnam Si, Gyeongggi Do, South Korea
[3] Hannam Univ, Dept Int Trade, 70 Hannam Ro, Daejeon, South Korea
基金
新加坡国家研究基金会;
关键词
NAVIGATION; DOMAIN; WATERWAYS; SYSTEM;
D O I
10.1155/2024/5062203
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Maritime collisions pose significant risks, prompting the need for robust risk assessment models to enhance safety measures. This study endeavors to develop a comprehensive ship collision risk model reflecting the intricate marine traffic environment in South Korea. Through a survey of experienced maritime personnel and a random forest model analysis, an evaluation model integrating internal and external factors was devised. Internal factors were determined through conjoint analysis, emphasizing encounter relationships, separation distance, and vessel speed. External risk factors were established using a random forest model based on historical collision data. The model's efficacy was then applied to and validated in the vicinity of Busan Port, a region with complex marine traffic. The resulting risk map highlighted high-risk areas, offering valuable insights for risk management and policy formulation. This model provides a foundational framework for maritime safety policy decisions, representing a significant contribution to collision risk assessment methodologies.
引用
收藏
页数:14
相关论文
共 58 条
  • [1] Spatial mapping of encounter probability in congested waterways using AIS
    Altan, Yigit C.
    Otay, Emre N.
    [J]. OCEAN ENGINEERING, 2018, 164 : 263 - 271
  • [2] [Anonymous], 2021, AP News
  • [3] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [4] An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system
    Bukhari, Ahmad C.
    Tusseyeva, Inara
    Lee, Byung-Gil
    Kim, Yong-Gi
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (04) : 1220 - 1230
  • [5] Early Warning Method and Model of Inland Ship Collision Risk Based on Coordinated Collision-Avoidance Actions
    Cheng, Zhiyou
    Li, Yaling
    Wu, Bing
    [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2020, 2020
  • [6] Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd
  • [7] MARINE TRAFFIC BEHAVIOR IN RESTRICTED WATERS
    COLDWELL, TG
    [J]. JOURNAL OF NAVIGATION, 1983, 36 (03) : 430 - 444
  • [8] A COMPUTER-SIMULATION OF MARINE TRAFFIC USING DOMAINS AND ARENAS
    DAVIS, PV
    DOVE, MJ
    STOCKEL, CT
    [J]. JOURNAL OF NAVIGATION, 1980, 33 (02) : 215 - 222
  • [9] TRAFFIC CAPACITY
    FUJII, Y
    TANAKA, K
    [J]. JOURNAL OF THE INSTITUTE OF NAVIGATION, 1971, 24 (04): : 543 - &
  • [10] STATISTICAL STUDY OF SHIP DOMAINS
    GOODWIN, EM
    [J]. JOURNAL OF NAVIGATION, 1975, 28 (03) : 328 - 344