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.

利用自动识别系统（Automatic Identification System，AIS）数据和编码器 - 解码器模型，本研究预测了船只轨迹，进而提高船只安全和避免船只与鲸类碰撞，证实了地理数据工程和轨迹预测模型在保护海洋生物物种方面的潜力。