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• Propose an adaptive compression algorithm involving multiple factors. • Develop a GPU parallel computing framework and a supportive compression method. • Design a new index for evaluating compression effectiveness. • Conduct comparative experiments using three substantial and representative datasets. Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era. [ABSTRACT FROM AUTHOR]

Traditionally, vessel air emissions are monitored onboard vessels or at fixed points at sea. These methods are not cost-effective for implementing emission control laws that address air pollution monitoring of vessels travelling over a large body of water. Unmanned aerial vehicles (UAVs) equipped with pollution monitoring sensors are becoming a research focus. However, due to battery capacity constraints, the monitoring scope of UAVs is still not optimal. Thus, using a ship (such as a patrol ship) as a UAV mobile supply base can overcome battery limitations and increase monitoring coverage. This paper investigates the joint routing and scheduling problem of ship-deployed multiple UAVs (SDMUs) for the monitoring of pollution from vessels. The artificial bee colony (ABC) algorithm based on simulated annealing is employed to minimize the total monitoring time. The model and solution algorithm are verified by real-time dynamic vessel data from Tianjin Port. [ABSTRACT FROM AUTHOR]