DSpace 集合:http://scholars.ntou.edu.tw/handle/123456789/2092024-03-26T12:46:14Z2024-03-26T12:46:14ZMaximizing Efficiency in the Suez Canal: A New Approach to Evaluate the Impact of Optimal Time-Varying Tolls on Ship Arrival TimesLaih, Chen-Hsiuhttp://scholars.ntou.edu.tw/handle/123456789/247052024-03-06T03:51:32Z2024-01-01T00:00:00Z標題: Maximizing Efficiency in the Suez Canal: A New Approach to Evaluate the Impact of Optimal Time-Varying Tolls on Ship Arrival Times
作者: Laih, Chen-Hsiu
摘要: In the existing literature, an optimal time-varying toll scheme has been proposed for the Suez Canal to address the inefficiency of numerous ships queuing and waiting at the anchorage area to enter the canal. The primary objective of this tolling strategy is to alleviate the significant issue of ships queuing at the canal's anchorage area. This stands in contrast to the current tolling system employed by the Suez Canal, which primarily aims to recover the management and operational costs associated with ship passage through the canal. However, the existing literature has yet to explore how the arrival times of ships at the anchorage area will change after implementing the optimal time-varying toll scheme. The goal is to ensure that the equilibrium cost of each tolled ship does not result in losses and achieve maximum efficiency in eliminating queueing at the anchorage area. To address this gap, this paper adopts the principle of cost equilibrium conservation and utilizes the Point-Slope Form to derive two mathematical formulas representing all ships' post-toll arrival times at the anchorage area of the Suez Canal. These formulas are specifically derived for two categories of tolled ships: those that enter the canal earlier than the latest entry time regulated by the canal authorities and those that enter later. The derived formulas are concise and comparative, strengthening the theoretical underpinnings of the current pricing model for a queuing canal. Furthermore, they serve as valuable references for canal authorities in devising pertinent measures, such as organizing the scheduling of canal pilots, to facilitate the implementation of the optimal time-varying toll scheme.2024-01-01T00:00:00ZResearch on the Collision Risk of Ships off Keelung Based on AIS DataChen, Shih-TzungYang, Ming-FengKao, Sheng-LongTu, MengruKuo, Jun -YuanChao, Yen-TingHsu, Huang-Kaihttp://scholars.ntou.edu.tw/handle/123456789/246692024-03-05T08:05:48Z2023-01-01T00:00:00Z標題: Research on the Collision Risk of Ships off Keelung Based on AIS Data
作者: Chen, Shih-Tzung; Yang, Ming-Feng; Kao, Sheng-Long; Tu, Mengru; Kuo, Jun -Yuan; Chao, Yen-Ting; Hsu, Huang-Kai
摘要: In previous literature, several computational methods have been proposed to analyze collision risks for vessels navigating at sea, most of which rely on the calculation of DCPA and TCPA between two vessels. However, this study adopts an enhanced version of the Vessel Conflict Ranking Operator (VCRO) to assess vessel collision risks. This approach not only considers the relative distance and relative velocity between two vessels but also takes their relative aspect into account. This methodology was applied to real-world vessels' dynamic data collected through AIS. From a near-collision perspective, it identifies high-risk areas near Keelung water where commercial vessels and fishing boats are more likely to collide. The hope is that in the near future, this method can be integrated into maritime collision warning systems (CWSs) of VTS (Vessel Traffic Service) and/or offshore wind power to enhance safety and navigation in maritime environments.2023-01-01T00:00:00ZShip Classification Based on AIS Data and Machine Learning MethodsHuang, I-LunLee, Man-ChunNieh, Chung-YuanHuang, Juan-Chenhttp://scholars.ntou.edu.tw/handle/123456789/246342024-03-05T07:53:27Z2024-01-01T00:00:00Z標題: Ship Classification Based on AIS Data and Machine Learning Methods
作者: Huang, I-Lun; Lee, Man-Chun; Nieh, Chung-Yuan; Huang, Juan-Chen
摘要: AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model's efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier's potential for maritime monitoring applications.2024-01-01T00:00:00ZRisk Assessment and Traffic Behaviour Evaluation of ShipsHuang, Juan-ChenUng, Shuen-Taihttp://scholars.ntou.edu.tw/handle/123456789/246142024-03-05T07:47:49Z2023-01-01T00:00:00Z標題: Risk Assessment and Traffic Behaviour Evaluation of Ships
作者: Huang, Juan-Chen; Ung, Shuen-Tai2023-01-01T00:00:00Z