COVID-19 Impact on Global Maritime Mobility

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of AIS receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: CNM of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet

A big data driven approach to extracting global trade patterns

Unlike roads, shipping lanes are not carved in stone. Their size, boundaries and content vary over space and time, under the influence of trade and carrier pat-terns, but also infrastructure investments, climate change, political developments and other complex events. Today we only have a vague understanding of the specific routes vessels follow when travelling between ports, which is an essen-tial metric for calculating any valid maritime statistics and indicators (e.g trade indicators, emissions and others). Whilst in the past though, maritime surveil-lance had suffered from a lack of data, current tracking technology has trans-formed the problem into one of an overabundance of information, as huge amounts of vessel tracking data are slowly becoming available, mostly due to the Automatic Identification System (AIS). Due to the volume of this data, traditional data mining and machine learning approaches are challenged when called upon to decipher the complexity of these environments. In this work, our aim is to transform billions of records of spatiotemporal (AIS) data into information for understanding the patterns of global trade by adopting distributed processing ap-proaches. We describe a four-step approach, which is based on the MapReduce paradigm, and demonstrate its validity in real world conditions

Detecting representative trajectories from global AIS datasets

<p>With real time vessel surveillance data now becoming available at an increasing rate, there is a growing interest in applications that can forecast future vessel positions and routes, especially in congested and busy areas. Since vessels move in “free space”, a prerequisite to effectively forecasting vessels' future locations is accurately discovering representative tracks (common paths followed by several vessels). Towards this direction, this work introduces a novel data driven framework that is capable of detecting spatial representations of complete trajectories (from port to port) from massive Automatic Identification System (AIS) datasets. Along these lines, we present a novel approach for forecasting representative tracks from noisy and non-uniform datasets (number of points, sampling rates, coverage gaps etc.) at a global scale. Our technique models the entire space where the vessels traveled in the past, detecting the set of frequently followed locations. This gives our proposed method the ability to forecast the most likely movement from a given query location towards a destination port. Finally, we present extensive experiments with real-world data, so as to demonstrate the effectiveness of our proposed method.</p&gt

Maritime Network Analysis: Connectivity and Spatial Distribution

International audienceIn this chapter we apply conventional graph-theoretical and complex network methods to a sample of port and inter-port shipping flows at and amongst the top 50 European ports in 2017 to detect the main topological and geographic structures of this network. Main results confirm earlier works by physicists about liner shipping network but our approach based on dry cargo and liquid cargo goes further with a mix of novelty and confirmation on how maritime networks and the European backbone in particular is driven by which forces

Validation and Application of the Accu-Waves Operational Platform for Wave Forecasts at Ports

This paper presents a recently developed Operational Forecast Platform (OFP) for prevailing sea conditions at very important ports worldwide (Accu-Waves). The OFP produces reliable high-resolution predictions of wave characteristics in and around ocean ports. Its goal is to support safer navigation, predict possible port downtime, assist vessel approaching, enhance management of towing services, and bolster secure ship maneuvering in busy ports around the globe. Accu-Waves OFP is based on integrated, high-resolution wave modelling over the continental shelf and in coastal areas that incorporates data from global- and regional-scale, open-sea wave and ocean circulation forecasts as boundary conditions. The coupling, nesting, calibration, and implementation of the models are reported and discussed in this paper, concerning 50 selected areas near and inside significant port basins. The detailed setup of the Accu-Waves OFP and its sub-system services is also provided regarding three-day forecasts at three-hourly intervals. The validation of the wave forecast system against in situ observations from wave buoys in coastal areas of the USA, Belgium, and Spain, as well as other model predictions by established OFPs, seems very promising, with performance skill scores ranging from adequate to very good. An exceptional case of stormy seas under severe marine weather conditions with very high wave maxima (>10 m) in the port of Algeciras is further discussed, confirming the good performance of the Accu-Waves OFP

Co-designed Innovation and System for Resilient Exascale Computing in Europe: From Applications to Silicon (EuroEXA)

EuroEXA targets to provide the template for an upcoming exascale system by co-designing and implementing a petascale-level prototype with ground-breaking characteristics. To accomplish this, the project takes a holistic approach innovating both across the technology and the application/system software pillars. EuroEXA proposes a balanced architecture for compute and data-intensive applications, that builds on top of cost-efficient, modular-integration enabled by novel inter-die links, utilises a novel processing unit and embraces FPGA acceleration for computational, networking and storage operations. EuroEXA hardware designers work together with system software experts optimising the entire stack from language runtimes to low-level kernel drivers, and application developers that bring in a rich mix of key HPC applications from across climate/weather, physical/energy and life-science/bioinformatics domains to enable efficient system co-design and maximise the impact of the project