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...

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines for each vessel. Vessels can have one or more tracks of any length, and can be separated by gaps due to intermittent loss of the AIS signal. Tracks will not necessarily start or stop at a well defined port, or when a vessel is not in motion. Vessel tracks are an efficient and spatially unbiased indicator of vessel traffic.

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...

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).

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.

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

This data file contains AIS vessel tracking records used in a study of low-frequency ocean noise off the California coast. Data span January through July of 2018 - 2020.

For the calculation of the data "AIS Vessel Density", the data of the Universal Shipborne Automatic Identification System (AIS) were evaluated with regard to various parameters and ship types under stochastic aspects. The data are requested once a year for the past year from the European Maritime Safety Agency (EMSA). Among others, the information is collected and stored for the purpose of securing maritime traffic and is used for the manufacture of products produced for navigation by the Federal Maritime and Hydrographic Agency (BSH). The data "AIS Vessel Density" represent the mean spatial density distribution of the ships. The mean spatial ship density is the current number of ships that could be expected in a defined area (grid cell) at any time during a reference period under consideration. The counting distinguishes between five types of vessels: fishing vessels, cargo vessels, tankers, passenger vessels and all vessels. For more information, please visit: https://gdi.bsh.de/en/data/Vessel-Density_Dokumentation_Schiffsdichte_DE.pdf

Automatic identification system (AIS) data are used to identify and track vessels for various purposes (primarily navigational safety). These data can be used to study vessel traffic, such as ship routing and speed over ground (SOG). Source data were obtained from the United States Coast Guard Navigation Center (USCG NAVCEN) for the period from June 2008 to December 2015. Derived data resulting from the processing of the source data are described here. This data set presents annual raster data (1 square kilometer grid size) off California from 2008-2015 for cumulative ship traffic density (kilometers/day) and mean SOG (knots; distance-weighted). The universe of data is limited to vessels with a length greater than or equal to 80 meters. The data are analyzed in three groups: freight vessels (container, general cargo, bulk carrier, refrigerated cargo, vehicle carrier, etc.), tanker vessels (crude oil, chemical/products, liquid petroleum gas, etc.) and all vessels (the previously noted vessels, plus passenger vessels and other vessel classes). The data are contained in a file geodatabase format as raster data sets. Metadata for the overall data set are contained at the level of the file geodatabase. The data were generated and used for a research article (Moore et al. 2018): Moore, T.J., Redfern, J.V., Carver, M., Hastings, S., Adams, J.D., Silber, G.K., 2018. Exploring Ship Traffic Variability off California. Ocean and Coastal Management. https://doi.org/10.1016/j.ocecoaman.2018.03.010 See this manuscript for more information on the data description, issues, and processing methods.

The global AIS Ship Tracking System market is booming, projected to reach $650 million by 2033 with an estimated 8% CAGR. Driven by increased maritime traffic and safety regulations, this market analysis explores key segments, leading companies, and regional trends in AIS technology. Discover market insights and future projections.

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 2011 for the contiguous United States offshore waters from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted...

Track the AM BERYL in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 538010718, IMO: 9377389

Explore the dynamic AIS Transponder market, projected to reach USD 80 million by 2025 with a robust CAGR. Discover key drivers, trends, and regional insights for maritime safety and vessel tracking solutions.

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.

Vessel traffic data or 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 management purposes. NOAA and BOEM have worked jointly to make available these data from the U.S. Coast Guards national network of AIS receivers. The original records were filtered to a one-minute frequency rate and were subsetted to depict the location and description of vessels broadcasting within the Exclusive Economic Zone. MarineCadastre.gov AIS data are divided by month and Universal Transverse Mercator (UTM) zone.

Track the FUHAI in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 371237000, IMO: 9300611

Track the GOLDEN WAVE in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 353587000, IMO: 9819911

Track the EVER ACT in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 352978199, IMO: 9893905

Track the LONG HAI 02 in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 574001260, IMO: 9597939

Track the PAUL in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 247155000, IMO: 9005819