Abstract
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.
Abstract (translated)
海上运输对全球经济增长和环境保护至关重要。在这方面，自动识别系统（AIS）数据通过提供关于船舶运动的实时流式数据发挥着重要作用，从而提高了交通监控，通过避免船舶之间的碰撞，以及主动预防船舶与鲸鱼的碰撞，有助于船舶安全。本文解决了轨迹预测的一个固有难题：利用工程序列的AIS数据进行多路径长短期记忆网络（Bi-LSTM）预测船舶的下一个12小时轨迹。我们将模型喂入由AIS数据生成的概率特征，这些特征指定了每个轨迹的潜在路线和目的地，以便模型利用卷积层进行空间特征学习，并具有位置感知注意机制，在时间特征学习过程中增加对序列最近时刻的重视，从而预测船舶轨迹时考虑潜在路线和目的地。这些特征的F1得分约为85%和75%，表明其补充神经网络的效率。我们在大西洋一个右旋鲸（NARW）的栖息地——大西洋北部海域的墨西哥湾进行试验，使用不同的技术和特征，取得了超过98%的R2得分。尽管高R2得分归因于定义良好的航运通道，但我们的模型在路径选择过程中表现出卓越的复杂决策能力。此外，我们的模型显示出增强的准确性，平均预测误差为11公里，中位数预测误差为6公里。我们的研究证实了地理数据工程和轨迹预测模型的潜在价值，可以用于保护海洋生物物种。
URL
https://arxiv.org/abs/2310.18948
