Table_1_Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay.DOCX

Vessel traffic management systems can be employed for environmental management where vessel activity may be of concern. One such location is in San Francisco Bay where a variety of vessel types transit a highly developed urban estuary. We analyzed vessel presence and speed across space and time using vessel data from the Marine Monitor, a vessel tracking system that integrates data from the Automatic Identification System and a marine-radar sensor linked to a high-definition camera. In doing so, we provide data that can inform collision risk to cetaceans who show an increased presence in the Bay and evaluation of the value in incorporating data from multiple sources when observing vessel traffic. We found that ferries traveled the greatest distance of any vessel type. Ferries and other commercial vessels (e.g., cargo and tanker ships and tug boats) traveled consistently in distinct paths while recreational traffic (e.g., motorized recreational craft and sailing vessels) was more dispersed. Large shipping vessels often traveled at speeds greater than 10 kn when transiting the study area, and ferries traveled at speeds greater than 30 kn. We found that distance traveled and speed varied by season for tugs, motorized recreational and sailing vessels. Distance traveled varied across day and night for cargo ships, tugs, and ferries while speed varied between day and night only for ferries. Between weekdays and weekends, distance traveled varied for cargo ships, ferries, and sailing vessels, while speed varied for ferries, motorized recreational craft, and sailing vessels. Radar-detected vessel traffic accounted for 33.9% of the total track distance observed, highlighting the need to include data from multiple vessel tracking systems to fully assess and manage vessel traffic in a densely populated urban estuary.

当船舶活动可能对环境造成影响并引发关切时，可借助船舶交通管理系统开展环境管理工作。旧金山湾便是这类场景之一：多种船舶类型穿梭于这片高度开发的城市河口区域。本研究借助船舶监测系统（Marine Monitor）的船舶数据，对时空维度下的船舶存在状态与航速展开分析，该系统整合了自动识别系统（Automatic Identification System）以及连接高清摄像头的海洋雷达传感器所采集的监测数据。通过上述分析，本研究生成的数据集可为两项工作提供决策参考：一是评估旧金山湾内种群数量有所增加的鲸类所面临的船舶碰撞风险；二是评估在船舶交通观测中融合多源数据的应用价值。研究发现，渡轮是各类船舶中累计航行距离最长的船型。渡轮与其他商用船舶（如货轮、油轮及拖轮）始终沿固定航道航行，而休闲船舶（如机动休闲艇与帆船）的航行轨迹则更为分散。大型运输船舶在研究区域内航行时，航速通常超过10节；渡轮的航速则可达30节以上。研究表明，拖轮、机动休闲艇与帆船的累计航行距离及航速均存在季节性差异。货轮、拖轮及渡轮的累计航行距离存在昼夜差异，而仅渡轮的航速存在昼夜变化。货轮、渡轮与帆船的累计航行距离存在工作日与周末的差异；而航速存在此类差异的船型则为渡轮、机动休闲艇及帆船。经雷达探测的船舶航行轨迹占总观测轨迹距离的33.9%，这凸显了在人口密集的城市河口区域开展船舶交通全面评估与管理时，融合多套船舶跟踪系统数据的必要性。