The Piraeus AIS Dataset for Large-scale Maritime Data Analytics

AIS data collected by the University of Piraeus' AIS receiver

Abstract

The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9^th, 2017 to December 26^th, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.

Dataset related to the following publication

Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2021.107782.

Files Description

• ais_static: CSV flat files containing vessels' static information and their corresponding types

• geodata: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)

• noaa_weather: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)

• unipi_ais_dynamic: CSV flat files containing AIS kinematic information 

• unipi_ais_dynamic_synopses: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions

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