Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figure

Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques

Increasing intensity in maritime traffic pushes the requirement in better preventionoriented incident management system. Observed regularities in data could help to predict vessel movement from previous vessels trajectory data and make further movement predictions under specific traffic and weather conditions. However, the task is burden by the fact that the vessels behave differently in different geographical sea regions, sea ports, and their trajectories depends on the vessel type as well. The model must learn spatio-temporal patterns representing vessel trajectories and should capture vessel’s position relation to both space and time. The authors of the paper proposes new unsupervised trajectory prediction with prediction regions at arbitrary probabilities using two methods: LSTM prediction region learning and wild bootstrapping. Results depict that both the autoencoder-based and wild bootstrapping region prediction algorithms can predict vessel trajectory and be applied for abnormal marine traffic detection by evaluating obtained prediction region in an unsupervised manner with desired prediction probability.&nbsp

Optimal weather routeing procedures for vessels on trans-oceanic voyages

Merged with duplicate record 10026.1/2726 on 06.20.2017 by CS (TIS)Three sets of algorithms are formulated for use in a variety of models :- * Ship performance algorithms. * Optimisation algorithms. * Environmental data. Optimisation models are constructed for deterministic minima, with time, fuel and cost objective functions. Models are constructed for an actual ship, (M. V. DART ATLANTIC), and realistic working solutions are obtained based on real-time weather information, simulating an actual on-board, computer based system, using dynamic programming. Several combinations of algorithm types are used in the the models, enabling comparisons of effectiveness. Thus, the ship performance algorithms incorporate severally; simple ship speed loss curves, ship resistance, ship motions and ship motion criteria databases devised from a linear seakeeping model. Limitations of the models are discussed from the routeing examples given. State space restrictions and originally devised methods to aid convergence in the models are discussed. Extension of the forecasted environmental data is achieved by a variety of methods and comparisons sought. In particular ECMWF surface pressure files are interrogated to produce sea wave fields over the extended period, establishing main disturbance centres. The variety of algorithms formulated in this work has facilitated real-time comparisons, this is particularly effective in route-updating. The development of these models and the methods used to extend the forecast period, and the comparisons and associated results stemming from these models are viewed as an original contribution to real-time weather routeing of ships.Oceanroutes (UK) Ltd and Oceanroutes Inc, US

Using Machine Learning to Predict Port Congestion : A study of the port of Paranaguá

Being able to accurately predict future levels of port congestion is of great value to both port and ship operators. However, such a prediction tool is currently not available. In this thesis, a Long Short-Term Memory Recurrent Neural Network is built to fulfill this need. The prediction model uses information mined from Automatic Identification Systems (AIS) data, vessel characteristics, weather data, and commodity price data as input variables to predict the future level of congestion in the port of Paranaguå, Brazil. All data used in this study are publicly available. The predictions of the proposed model are shown to be promising with a satisfactory level of accuracy. The conclusion and evaluation of the presented model are that it serves its purpose and fulfills its objective within the constraints set by the authors and its inherent limitations.nhhma

Toward Using High-frequency Coastal Radars for Calibration of S-AIS Based Ocean Vessel Tracking Models

Most of the world relies on ships for transportation, shipping, and tourism. Automatic Identification System messages are transmitted from ships and provide a wealth of positional data on these open ocean vessels. This data is being utilized to determine the optimal path for ships, as well as predicting where a ship may be going in the near future. It has only been in the past decade that Automatic Identification Systems (AIS) signals have been easily received with satellites (S-AIS) so there have been few studies that look at using available information and pairing it with the new abundance of ship positional data. This study attempts to use High Frequency (HF) radar data that measures the velocity of surface ocean currents off the West Coast of North America and incorporates North Pacific Automatic Identification Systems data to create a basic prediction model that uses the radar data to refine the positional accuracy of the prediction. Determining the effects of ocean currents on a ship using these data sets allows for later calibration of currently available position prediction models using high frequency radar data. While the study was unable to obtain consistent prediction correlation results the work systematically analyzes inconstancy and limitations of existing S-AIS and HF radar data that is a valuable contribution to the field

Path planning with spatiotemporal optimal stopping for stochastic mission monitoring

© 2017 IEEE. We consider an optimal stopping formulation of the mission monitoring problem, in which a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a predicted trajectory. This problem arises in a diverse range of scenarios, such as autonomous underwater vehicles supervised by surface vessels, pedestrians monitored by aerial vehicles, and animals monitored by agricultural robots. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, robot motion is modeled in general as a stochastic process, and observations are modeled as a spatial probability distribution. We propose a resolution-complete algorithm that runs in a polynomial time. The algorithm is based on a sweep-plane approach and generates a motion plan that maximizes the expected observation time and value. A variety of stochastic models may be used to represent the robot trajectory. We present results with data drawn from real AUV missions, a real pedestrian trajectory dataset and Monte Carlo simulations. Our results demonstrate the performance and behavior of our algorithm, and relevance to a variety of applications

Multiple-Aspect Analysis of Semantic Trajectories

This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification