A vessel track shows the location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area. The vessel track positions in this data set are collected and recorded from land-based antennas as part of a national network operated by the U.S. Coast Guard.

About Dataset

Context

This dataset contains processed Automatic Identification System (AIS) data for vessel traffic analysis. Unlike raw AIS logs, this version has been heavily pre-processed and feature-engineered for machine learning tasks like ETA Prediction (Regression) and Vessel Movement Classification.

Content

The dataset contains vessel dynamics, physical characteristics, and engineered features calculated relative to destination clusters.

1. Identifiers & Time: * MMSI, IMO, CallSign, VesselName: Unique identifiers for the ships. * BaseDateTime: Timestamp of the recorded position.

2. Kinematics (Movement): * LAT, LON: Current geographical coordinates. * SOG (Speed Over Ground) & SOG_kmh: Speed in knots and converted to km/h. * COG (Course Over Ground) & Heading: Direction of movement and bow orientation. * Speed_Category: Binned speed status (e.g., "Stopped", "Slow").

3. Vessel Physicals: * Length, Width, Draft: Physical dimensions in meters. * VesselType, Status, Cargo: Raw numeric codes defining ship category and state. * Transceiver: Class A or B transceiver type.

4. Engineered / Predictive Features: * dest_cluster: Clustered destination ID (derived from frequent stopping points). * dest_lat, dest_lon: Coordinates of the predicted destination. * dist_km: Calculated distance from current position to destination. * ETA_min, ETA_hours: Estimated Time of Arrival (calculated target variables).

Processing Steps

• Imputation: Missing heading/draft values filled with mean/mode.
• Feature Engineering: Converted SOG to km/h; calculated haversine distance (dist_km) to destination clusters.
• Encoding: Created numerical encodings for categorical fields (Status_enc, VesselType_enc).

A vessel track shows the location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a...

A vessel track shows the location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area. The vessel track positions in this data set are collected and recorded from land-based antennas as part of a national network operated by the U.S. Coast Guard.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the m...

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of vessel traffic in 2013 for the contiguous United States offshore waters from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted...

Nowadays, a multitude of tracking systems produce massive amounts of maritime data on a daily basis. The most commonly used is the Automatic Identification System (AIS), a collaborative, self-reporting system that allows vessels to broadcast their identification information, characteristics and destination, along with other information originating from on-board devices and sensors, such as location, speed and heading. AIS messages are broadcast periodically and can be received by other vessels equipped with AIS transceivers, as well as by on the ground or satellite-based sensors.

Since becoming obligatory by the International Maritime Organisation (IMO) for vessels above 300 gross tonnage to carry AIS transponders, large datasets are gradually becoming available and are now being considered as a valid method for maritime intelligence [4].There is now a growing body of literature on methods of exploiting AIS data for safety and optimisation of seafaring, namely traffic analysis, anomaly detection, route extraction and prediction, collision detection, path planning, weather routing, etc., [5].

As the amount of available AIS data grows to massive scales, researchers are realising that computational techniques must contend with difficulties faced when acquiring, storing, and processing the data. Traditional information systems are incapable of dealing with such firehoses of spatiotemporal data where they are required to ingest thousands of data units per second, while performing sub-second query response times.

Processing streaming data seems to exhibit similar characteristics with other big data challenges, such as handling high data volumes and complex data types. While for many applications, big data batch processing techniques are sufficient, for applications such as navigation and others, timeliness is a top priority; making the right decision steering a vessel away from danger, is only useful if it is a decision made in due time. The true challenge lies in the fact that, in order to satisfy real-time application needs, high velocity, unbounded sized data needs to be processed in constraint, in relation to the data size and finite memory. Research on data streams is gaining attention as a subset of the more generic Big Data research field.

Research on such topics requires an uncompressed unclean dataset similar to what would be collected in real world conditions. This dataset contains all decoded messages collected within a 24h period (starting from 29/02/2020 10PM UTC) from a single receiver located near the port of Piraeus (Greece). All vessels identifiers such as IMO and MMSI have been anonymised and no down-sampling procedure, filtering or cleaning has been applied.

The schema of the dataset is provided below:

· t: the time at which the message was received (UTC)

· shipid: the anonymized id of the ship

· lon: the longitude of the current ship position

· lat: the latitude of the current ship position

· heading: (see: https://en.wikipedia.org/wiki/Course_(navigation))

· course: the direction in which the ship moves (see: https://en.wikipedia.org/wiki/Course_(navigation))

· speed: the speed of the ship (measured in knots)

· shiptype: AIS reported ship-type

· destination: AIS reported destination

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell.

These data are a spatially explicit representation of monthly shipping intensity in the Pacific Arctic region from January 1, 2015 to December 31, 2020. We calculated shipping intensity based on Automatic Identification System (AIS) data, a type of Global Positioning System (GPS) transmitter required by the International Maritime Organization on all ships over 300 gross tonnes on an international voyage, all cargo ships over 500 gross tonnes, and all passenger ships. We used AIS data received by the exactEarth satellite constellation (64 satellites as of 2020), ensuring spatial coverage regardless of national jurisdiction or remoteness. Our analytical approach converted raw AIS input into monthly hex datasets. We first filtered raw AIS messages to remove spurious records and GPS errors, then joined remaining vessel positional records with static messages including descriptive attributes. We further categorized these messages into one of four general ship types (cargo; tanker; fishing; and other). For the vector dataset, we spatially intersected AIS messages with a hexagon (hex) grid and calculated the number of unique ships, the number of unique ships per day (summed over each month), and the average and standard deviation of the speed over ground. We calculated these values for each month for all vessels as well as vessels subdivided by ship type and for messages from vessels greater than 65 feet long and traveling at greater than 10 knots. These monthly datasets provide a critical snapshot of dynamic commercial and natural systems in the Pacific Arctic region. Recent declines in sea ice have lengthened the duration of the shipping season and have expanded the spatial coverage of large vessel routes, from the Aleutian Islands through the Bering Strait and into the southern Chukchi Sea. As vessel traffic has increased, so has exposure to the myriad environmental risks posed by large ships, including oil spills, underwater noise pollution, large cetacean ship-strikes, and discharges of pollutants. This dataset provides scientific researchers, local community members, mariners, and decision-makers with a quantitative means to evaluate the distribution and intensity of shipping across space and through time. In addition to these hex data, we also produced data products in 25- and 10-km raster format as well as a 1-km coastal data subset. To find these products, search for “North Pacific and Arctic Marine Vessel Traffic Dataset” in the Arctic Data Center’s data repository.

Terrestrial AIS (T-AIS) data from the Western Mediterranean (map inset Fig 5a) were collated by the Balearic Islands Coastal and Forecasting System (SOCIB) using a real-time operational system connected to a web-service provided by MarineTraffic (https://www.marinetraffic.com/). The database used in this study contained AIS data from 1st January 2016 until 30th June 2020 at 5 minute intervals (> 545 million AIS messages). In addition to the vessel tracks, the database also included information associated with each vessel, such as the vessel type or length. A first pre-processing of the raw data included the removal of duplicates, invalid identification numbers (i.e. Maritime Mobile Service Identity -MMSI- codes without 9 digits) and codes outside the correct numerical range (i.e. MMSI codes with first digits between 2 and 7 are those intended for individual ships). In order to address inconsistencies in the vessel and MMSI combinations (e.g., changes of MMSI across years), we selected the more frequent combination of MMSI and vessel characteristics (e.g. vessel name and vessel type) for each calendar year. We used a similar vessel categorization as the S-AIS dataset, but were able to derive a sixth category from the AIS metadata, separating “recreational” vessels from “other” vessels. Therefore, vessels were classified into six categories: cargo, tanker, passenger (included high speed crafts and passenger vessels), fishing, recreational (included sailing vessels and pleasure crafts), and others (included all other ship types). We excluded ship type codes 20 to 29 (i.e. wing-in-ground-effect and search and rescue aircraft), as well as codes that had an invalid value (i.e. empty or null) or the value was not listed in the previous type codes. We calculated the number of vessels per day taking into account only those that were underway, thus removing moored vessels inside ports that were inactive. T-AIS coverage was not homogenous in the study area (Holmes et al. 2020) due to a non-uniformly distribution of antennas (i.e. few antennas in north Africa, see www.marinetraffic.com). Based on a previous analysis of the AIS coverage (Holmes et al 2020), we filtered vessels within the coastal zone (44.4 km, ~24 nautical miles) of EU countries (i.e. a total area of 164,318.2 km2 comprised by Spain, France and Italy), thus reducing potential bias due to temporal gaps in signal reception.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell.

Track the MENAWETHAN in real-time with AIS data. Tradlinx provides live vessel position, speed, and course updates. Search by MMSI: 232059791, IMO: 1056367

Live AIS-derived positions, types, flags and identifiers for 55,000+ commercial vessels worldwide, refreshed continuously from terrestrial and satellite AIS feeds.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. Information such as location, time, ship type, length, width, and draft have been extracted from the raw data and prepared as track lines for analyses in desktop GIS software.

Real-time and historical AIS vessel position data for Homer, Alaska.

Track the PRESIDENT REAGAN in real-time with AIS data. Tradlinx provides live vessel position, speed, and course updates. Search by MMSI: 368680000, IMO: 9938341

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell.

Track the CHARLESTON in real-time with AIS data. Tradlinx provides live vessel position, speed, and course updates. Search by MMSI: 368119660, IMO: 1292926

Track the INDUSTRIAL SKIPPER in real-time with AIS data. Tradlinx provides live vessel position, speed, and course updates. Search by MMSI: 636017392, IMO: 9741138

Track the ATLAS in real-time with AIS data. Tradlinx provides live vessel position, speed, and course updates. Search by MMSI: 366939170, IMO: 8851144