Assessing vessel transportation delays affected by tropical cyclones using AIS data and a bayesian network: A case study of veronica in northwestern Australia
编号:579 访问权限:仅限参会人 更新:2024-10-12 14:06:59 浏览:50次 张贴报告

报告开始:2025年01月15日 18:05(Asia/Shanghai)

报告时间:15min

所在会场:[S4] Session 4-Extreme Weather and Climate Events: Observations and Modeling [S4-P] Extreme Weather and Climate Events: Observations and Modeling

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摘要
Tropical cyclones frequently disrupt maritime transportation systems, impacting the normal operation of vessels and causing transportation delays. Analyzing the occurrence and degree of vessel transportation delays under disaster conditions is one of the critical aspects of maritime transportation management. This article, based on Automatic Identification System (AIS) data, examined the real behavioral trajectories of vessels and delay characteristics during tropical cyclones. Taking the example of Tropical Cyclone Veronica, which occurred in the waters off northwestern Australia in 2019, we conducted a comprehensive analysis. This involved numerical simulations of the entire disaster process and the cleaning and matching of trajectory data for all affected vessels in the area. We delved deeply into the relationship between vessel behavior characteristics and tropical cyclones, identified factors contributing to delays, and, based on our findings, constructed a Bayesian network inference model for vessel transportation delays under disaster conditions. This model uses information such as tropical cyclone intensity, vessel basic attributes, behavior choices, and port disaster avoidance measures as its primary nodes. The research results indicate that vessel rerouting, vessel loss of control, and waiting for port entry are the three major sources of vessel delays during disasters. Key influencing factors contributing to these consequences include port closure measures, the duration of vessels being adversely affected by strong winds, vessel tendencies toward loss of control, and risk preferences. To control the extent of vessel delays more effectively, it is crucial to prioritize these sensitive factors and strengthen monitoring and management efforts. This model is driven by real data, providing an objective reflection of real-world scenarios, and its effectiveness has been validated through sensitivity analysis. The research perspective and algorithmic framework presented in this paper offer a novel paradigm and fresh perspective for the study of maritime transportation disasters. The research findings have significant implications for bolstering the resilience of maritime transportation networks, enhancing our comprehension and anticipation of delays, and ensuring the continuity, security, and efficiency of the global supply chain for goods.
关键词
Tropical cyclone,AIS,transport efficiency
报告人
Caixia Zhang
Associate Researcher peking university shenzhen

稿件作者
Caixia Zhang peking university shenzhen
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重要日期
  • 会议日期

    01月14日

    2025

    01月17日

    2025

  • 09月27日 2024

    初稿截稿日期

  • 12月14日 2024

    注册截止日期

主办单位
State Key Laboratory of Marine Environmental Science, Xiamen University
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