Ship collision avoidance behaviour recognition and analysis based on AIS data

被引:60
作者
Rong, H. [1 ]
Teixeira, A. P. [1 ]
Soares, C. Guedes [1 ]
机构
[1] Univ Lisbon, Ctr Marine Technol & Ocean Engn CENTEC, Inst Super Tecn, Av Rovisco Pais, P-1049001 Lisbon, Portugal
关键词
Ship evasive manoeuvring; Collision avoidance; Sliding window Algorithm; Near collision scenario; RISK-ASSESSMENT; SAFETY; DOMAIN; NAVIGATION; MANEUVERS; SYSTEM; TRAJECTORIES; COMPRESSION; PREDICTION; ACCIDENTS;
D O I
10.1016/j.oceaneng.2021.110479
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
A novel approach is proposed to automatically identify the ship collision avoidance behaviour from ship trajectories based on an improved Sliding Window Algorithm. The approach has three main stages: (1) determining the ships' obligations according to the Convention on the International Regulations for Preventing Collision at Sea (COLREGs); (2) assessing the rudder angle according to the true bearing of the target ship; (3) identifying the corresponding ship handling behaviour from the ship trajectory taking into account the Rate of Turn and its derivative based on the Sliding Window Algorithm. Four typical encounter scenarios are studied to demonstrate the feasibility and effectiveness of the proposed method. The approach is then applied to near collision scenarios identified in the maritime traffic off the continental coast of Portugal, which allowed the characterization of the relative and spatial distributions of the locations at which the ships take evasive manoeuvres. The results show that the approach can be applied to accurately detect the ship collision avoidance behaviour from Automatic Identification System trajectory data, and the characterization of the collision avoidance behaviour can potentially be used by situational awareness systems and as the basis for ship collision avoidance decision-making.
引用
收藏
页数:20
相关论文
共 93 条
  • [81] A two-stage black-spot identification model for inland waterway transportation
    Zhang, Jinfen
    Wan, Chengpeng
    He, Anxin
    Zhang, Di
    Soares, C. Guedes
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 213
  • [82] Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks
    Zhang, Jinfen
    Teixeira, Angelo P.
    Guedes Soares, C.
    Yan, Xinping
    Liu, Kezhong
    [J]. RISK ANALYSIS, 2016, 36 (06) : 1171 - 1187
  • [83] Probabilistic ship domain with applications to ship collision risk assessment
    Zhang, Liye
    Meng, Qiang
    [J]. OCEAN ENGINEERING, 2019, 186
  • [84] Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters
    Zhang, Liye
    Meng, Qiang
    Fwa, Tien Fang
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2019, 129 : 287 - 304
  • [85] A novel ship trajectory reconstruction approach using AIS data
    Zhang, Liye
    Meng, Qiang
    Xiao, Zhe
    Fu, Xiuju
    [J]. OCEAN ENGINEERING, 2018, 159 : 165 - 174
  • [86] Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity
    Zhang, Shu-kai
    Shi, Guo-you
    Liu, Zheng-jiang
    Zhao, Zhi-wei
    Wu, Zhao-lin
    [J]. OCEAN ENGINEERING, 2018, 155 : 240 - 250
  • [87] AIS Trajectories Simplification and Threshold Determination
    Zhang, Shu-kai
    Liu, Zheng-jiang
    Cai, Yao
    Wu, Zhao-lin
    Shi, Guo-you
    [J]. JOURNAL OF NAVIGATION, 2016, 69 (04) : 729 - 744
  • [88] An advanced method for detecting possible near miss ship collisions from AIS data
    Zhang, Weibin
    Goerlandt, Floris
    Kujala, Pentti
    Wang, Yinhai
    [J]. OCEAN ENGINEERING, 2016, 124 : 141 - 156
  • [89] A method for detecting possible near miss ship collisions from AIS data
    Zhang, Weibin
    Goerlandt, Floris
    Montewka, Jakub
    Kujala, Pentti
    [J]. OCEAN ENGINEERING, 2015, 107 : 60 - 69
  • [90] Spatial patterns and characteristics of global maritime accidents
    Zhang, Yang
    Sun, Xukai
    Chen, Jihong
    Cheng, Cheng
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 206