Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction

Deep Learning & Graph Clustering for Maritime Logistics: Predicting Destination and Expected Time of Arrival for Vessels Across Europe

In recent years, the need for improving operational processes internationally has drastically increased in the maritime logistics field. The lack of streamlined systems that provide reliable information about real-time maritime traffic for the main agents across countries, such as ports operators and ships authorities, has prompted several research questions. In this work, we propose Deep learning and Machine Learning based methods for (i) clustering ports across Europe using their maritime traffic connectivity, (ii) predicting the next destination of vessels, and (iii) forecasting their expected voyage duration. Several experiments based on public AIS data are developed to analyse and verify these methods, and the results of these experiments indicate that the proposed models achieve the state-of-the-art predictive performance considering the wide geographical scope of the problem across all over Europe. Furthermore, a big advantage of the proposed methods respect to other solutions is that the input data configuration and the intrinsic nature of the models enable the users to predict the aforementioned targets about the next destination of vessels right after they arrive at any European port, instead of waiting for the information given by the first submitted AIS messages once their corresponding next voyage has started. When deployed into production, the resulting system will help maritime industry agents to enhance their real-time situational awareness and operational planning

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

[EN] Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.Funding This work has been developed under the framework of the COSIBAS project (funded by CDTI research and innovation programme under grant agreement No.EXP 00110912/INNO-20181033) and the EIFFEL project (funded by European Unions Horizon 2020 research and innovation programme under grant agreement No 101003518).Valero López, CI.; Ivancos Pla, E.; Vañó García, R.; Garro, E.; Boronat, F.; Palau Salvador, CE. (2021). Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution. Sensors. 21(23):1-15. https://doi.org/10.3390/s21238133115212

Predicting Marine Traffic in the Ice-Covered Baltic Sea

Icebreaking activity and seasonal ice propose challenges for marine traffic prediction in the Baltic Sea. Traffic prediction is a vital part in the planning of icebreaking activities, but it remains largely as a manual task. The aim of this thesis is to examine factors influencing marine traffic modelling in ice-covered waters and propose a novel A*-based method for modelling traffic in ice. The current state of the marine traffic modelling and factors affecting vessel movement are concluded by examining the literature and historical vessel tracks. The field of traffic modelling research is growing rapidly. Currently the biggest challenges are evaluation of results and the lack of publicly available datasets. Moreover, the current approaches to model vessel movement in ice are promising but fail to capture how icebreaking activity influences vessel routes. The proposed model consists of sea, maneuverability, route and speed modelling. The model uses historical AIS data, topography of the sea, vessel type and dirways as main data inputs. The model is trained with summer tracks and dirways are used for modelling the ice channels kept open by icebreakers. The accuracy of the model is evaluated by examining route, speed, traffic and ETA (estimated time of arrival) prediction results separately. Moreover, the area between the actual and predicted route is introduced as an accuracy measure for route prediction. The model shows that winter route prediction can be improved by incorporating dirways to the modelling. However, the use of dirways did not affect the speed, traffic or ETA prediction accuracy. Finally, the datasets and source code used in this thesis are published online

Predicting Marine Traffic in the Ice-Covered Baltic Sea

Icebreaking activity and seasonal ice propose challenges for marine traffic prediction in the Baltic Sea. Traffic prediction is a vital part in the planning of icebreaking activities, but it remains largely as a manual task. The aim of this thesis is to examine factors influencing marine traffic modelling in ice-covered waters and propose a novel A*-based method for modelling traffic in ice. The current state of the marine traffic modelling and factors affecting vessel movement are concluded by examining the literature and historical vessel tracks. The field of traffic modelling research is growing rapidly. Currently the biggest challenges are evaluation of results and the lack of publicly available datasets. Moreover, the current approaches to model vessel movement in ice are promising but fail to capture how icebreaking activity influences vessel routes. The proposed model consists of sea, maneuverability, route and speed modelling. The model uses historical AIS data, topography of the sea, vessel type and dirways as main data inputs. The model is trained with summer tracks and dirways are used for modelling the ice channels kept open by icebreakers. The accuracy of the model is evaluated by examining route, speed, traffic and ETA (estimated time of arrival) prediction results separately. Moreover, the area between the actual and predicted route is introduced as an accuracy measure for route prediction. The model shows that winter route prediction can be improved by incorporating dirways to the modelling. However, the use of dirways did not affect the speed, traffic or ETA prediction accuracy. Finally, the datasets and source code used in this thesis are published online

Economic aspects of automation innovations in electronic transportation management systems

This paper presents an analysis of economic aspects of three selected automation innovations in electronic Transportation Management Systems: Maritime Transport Chain solution and Vessel Estimated Time of Arrival solution (related to the maritime transport) and Delivery Planning solution (related to the transport in general). The theoretical background of transportation, Transportation Management Systems, maritime transportation and seaports is provided, focusing on the economic aspects. A literature review has been conducted, in order to identify the research gap and to focus on the economic aspects of the selected automation innovations. A SWOT Analysis of the Maritime Transport Chain solution, Vessel Estimated Time of Arrival solution and Delivery Planning solution (from an internal and external perspective) is presented, adding to the existing research of the economic aspects of automation innovations in the transport sector

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

Big Data Management in Maritime Transport

As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport

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