Abstract Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.

Maritime transport systems are essential to human mobility. A vital part of the maritime transport systems is the accurate vessel route forecasting (VRF). However, accurate VRF is a challenging task due to the fact that maritime traffic conditions are complex and dynamic. Machine learning (ML) methods can leverage from the ”explosion” of vessel surveillance information in order to encourage, enable deeper digitalization in the shipping industries and tackle the VRF problem. In this paper we investigate some of the most popular ML methods to address the VRF problem and we present an overview of these methods through an experimental testbed based on real vessel surveillance data. This work results in introducing baseline ML models for VRF purposes.

Within the last years the shipping industry invest-ments continue to grow to improve maritime transport systems. A vital part of the maritime transport systems is the accurate Vessel Traffic Flow Forecasting (VTFF). In this paper, we approach the VTFF problem from two different perspectives: a) indirect - as a vessel route forecasting application via employing predicted vessels locations in the future, and b) direct - as a flow sequence forecasting problem. In both strategies, machine learning methods are employed because they can leverage from the massive vessel surveillance information to enable deeper digitalization in the shipping industry. This work performs an experimental comparative study between the two approaches over a real dataset from the maritime domain.

The wide spread of Automatic Identification System (AIS) and tools based on it has motivated several maritime analytics operations. One of the most critical operations for the purpose of maritime safety is the so-called Vessel Collision Risk Assessment (VCRA). Accurate VCRA is a challenging task as maritime traffic is quite volatile, often affected by external factors, such as weather, etc. Addressing this problem by using complex models introduces a trade-off between accuracy quality and responsiveness. On the other hand, Machine Learning (ML) methods can better address this tradeoff. In this paper, we study the VCRA problem from the ML perspective, by proposing an architecture based on the Multi-Layered Perceptron (MLP) model. Our preliminary experimental study over a large-scale AIS dataset shows that the proposed methodology outperforms the kinematic equations-based approach.

The i4sea research project provides effective and efficient big data integration, processing and analysis technologies to deliver both real-time and historical operational snapshots of fishing vessels activity in national sea areas. This paper presents the architecture of the i4sea big data platform for sea area monitoring and analysis of fishing vessels activity and demonstrates the operation of some use-case pilot scenarios.

We present a big data framework for the prediction of streaming trajectory data, enriched from other data sources and exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. To meet this goal, we follow a multi-step methodology. First, we efficiently compress surveillance data in an online fashion, by constructing trajectory synopses that are spatio-temporally linked with streaming and archival data from a variety of diverse and heterogeneous data sources. The enriched stream of trajectory synopses is stored in a distributed RDF store, supporting data exploration via SPARQL queries. The enriched stream of synopses along with the raw data is consumed by trajectory prediction algorithms that exploit mined patterns from the RDF store, namely medoids of (sub-) trajectory clusters, which prolong the horizon of useful predictions. The framework is extended with offline and online interactive visual analytics tool to facilitate real world analysis in the maritime and the aviation domains.