Maritime transport is paramount to global economic growth and environmental sustainability. In this regard, the Automatic Identification System (AIS) data plays a significant role by offering real-time streaming data on vessel movement, which allows for enhanced traffic surveillance, assisting in vessel safety by avoiding vessel-to-vessel collisions and proactively preventing vessel-to-whale ones. This paper tackles an intrinsic problem to trajectory forecasting: the effective multi-path long-term vessel trajectory forecasting on engineered sequences of AIS data. We utilize an encoder-decoder model with Bidirectional Long Short-Term Memory Networks (Bi-LSTM) to predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data. We feed the model with probabilistic features engineered from the AIS data that refer to the potential route and destination of each trajectory so that the model, leveraging convolutional layers for spatial feature learning and a position-aware attention mechanism that increases the importance of recent timesteps of a sequence during temporal feature learning, forecasts the vessel trajectory taking the potential route and destination into account. The F1 Score of these features is approximately 85% and 75%, indicating their efficiency in supplementing the neural network. We trialed our model in the Gulf of St. Lawrence, one of the North Atlantic Right Whales (NARW) habitats, achieving an R2 score exceeding 98% with varying techniques and features. Despite the high R2 score being attributed to well-defined shipping lanes, our model demonstrates superior complex decision-making during path selection. In addition, our model shows enhanced accuracy, with average and median forecasting errors of 11km and 6km, respectively. Our study confirms the potential of geographical data engineering and trajectory forecasting models for preserving marine life species.

翻译：全球经济增长与环境可持续发展高度依赖海上运输。自动识别系统（AIS）数据通过提供船舶运动的实时流数据，在增强交通监控、避免船舶碰撞事故以及主动预防船舶与鲸类碰撞方面发挥着重要作用。本文旨在解决轨迹预测领域的核心问题——基于结构化AIS数据序列实现高效的多路径长期船舶轨迹预测。我们采用编码器-解码器架构，利用双向长短期记忆网络（Bi-LSTM），基于1至3小时的AIS数据预测未来12小时的船舶轨迹。模型输入包含从AIS数据中提取的概率特征，这些特征表征每条轨迹的潜在航行路线与抵达目的地。通过引入用于空间特征学习的卷积层，以及增强时序特征学习中近期时间步重要性的位置感知注意力机制，模型能够在考虑潜在路径和终点的条件下完成轨迹预测。这些特征的F1分数分别达到约85%和75%，证明了其对神经网络的有效补充。我们在北大西洋露脊鲸（NARW）栖息地之一的圣劳伦斯湾进行模型评估，通过不同技术与特征的组合，模型R2分数超过98%。尽管高R2分数归因于明确的航道特征，但我们的模型在路径选择决策中展现出卓越的复杂情境处理能力。此外，模型预测精度显著提升，平均预测误差与中位误差分别为11公里和6公里。本研究验证了地理数据工程与轨迹预测模型在保护海洋生物物种方面的应用潜力。