Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams

The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode

Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score

Mapping EU fishing activities using ship tracking data

Information and understanding of fishing activities at sea are fundamental components of marine knowledge and maritime situational awareness. Such information is important to fisheries science, public authorities and policy makers. In this paper we introduce a first map at European scale of EU fishing activities extracted using Automatic Identification System (AIS) ship tracking data. The resulting map is a density of points that identify fishing activities. A measure of the reliability of such information is also presented as a map of coverage reception capabilities.Comment: Paper accepted for publicatio

Automatic Identification System (AIS): An initiative in purse seine fisheries along Mumbai coast

Automatic Identification System (AIS) is a significant development in navigation safety since the introduction of RADAR. It was originally developed as a collision avoidance tool for commercial vessels to improve the helmsman’s information about his surrounding environment. AIS does this by continuously transmitting a vessels identity, position, speed and course along with other relevant information to all other AIS equipped vessels within range

Completeness and Accuracy of a Wide-Area Maritime Situational Picture based on Automatic Ship Reporting Systems

Automatic ship reporting systems (AIS – Automatic identification System, LRIT – Long Range Identification and Tracking, VMS – Vessel Monitoring System) today allow global tracking of ships. One way to display the results is in a map of current ship positions over an area of interest, the Maritime Situational Picture (MSP). The MSP is dynamic and must be constructed from fusing the reporting systems’ messages, constructing ship tracks and predicting ship positions to correct for latency especially in the case of AIS received by satellite which forms the bulk of the data. This paper discusses the completeness of the resulting MSP and the accuracy of its positions, quantifying the additional value of the individual data sources.JRC.G.3-Maritime affair

Calculation accuracy of safe course made good in an anticollision system

The article presents an accuracy analysis of calculation made by aMultiagent Decision-Support System (MADSS) of navigation. On the basis of messages received from Universal Ship-borne AIS system (Automatic Identification System) the system calculates the parameters of vessels’ encounter and works out the parameters of own vessel’s movement (course or speed), which lead to passing other objects according to a set CPA (Closest Point of Approach)

Proposed AIS Binary Message Format Using XML for Providing Hydrographic-related Information

UNH is working with the USCG and NOAA to use XML (Extensible Markup Language) to define binary messages for maritime-based AIS (Automatic Identification System). A draft specification format is under development that will enable hydrographic and maritime safety agencies to encode AIS message contents by providing a bit-level description in XML (informally known the AIS Binary Message Decoder Ring ). An AIS binary message definition in XML specifies the order, length, and type of fields following a subset of that used by the ITU-R.M.1371-1. The specification is independent of programming language (e.g., can be implemented in C, C++, C#, Java, Python, etc.) to allow vendors to integrate the system into their individual design requirements. The draft specification also contains a reference implementation of an AIS XML to Python compiler that has been released as open-source under the GNU General Public License (GPL) version 2. A XML schema and an additional program will provide validation of the XML message definitions. A XSLT style sheet produces reference documentation in ‘html’ format. Although the XML message definition file specifies the order, size, and type of the bit stream, it does not specify semantics or how binary messages should be displayed on a shipboard ECDIS, or presented on other shipboard/shore-side display devices

Marine Ship Automatic Identification System (AIS) for Enhanced Coastal Security Capabilities: An Oil Spill Tracking Application

National and international trade via shipping is already significant, and expected to continue increasing rapidly over the next decade. Both more ships and larger ships will contribute to this trade, includingships from countries with less rigorous shipping maintenance and inspection standards than the United States, and less strict pollution monitoring regulations. Changes in ship traffic management protocols have been implemented in recent years in the U.S. to minimize damage to coastlines, particularly near sensitive or protected marine environments. For example, to reduce risk to coastal resources off central California, shipping lanes for larger vessels were moved further offshore to allow for additional response time in case of accidents before such vessels might drift into coastal areas. Similarly, shipsare now routed via specific approach channels when entering Boston Harbor to reduce impacts within adjacent National Marine Sanctuary resources. Several recent high profile cases have occurred where \u27mystery\u27 oil spills were found near shipping channels, but no vessel could be readily identified as their source. These incidents lead to extensive and expensive efforts to attempt to identify the shipsresponsible. As time passes in responding to these incidents, the likelihood of confirming the identity of the ships diminishes. Unfortunately, reports of vessels engaging in illegal oily waste discharge to reduce fees for offloading the waste in port are ongoing. We here discuss use of improved capabilities of near-continuous real-time position location monitoring of shipping traffic using marine AutomaticIdentification Systems (AIS) for ships that would facilitate identification of ships responsible for illegal oily waste discharge. The next phase of the National AIS, N-AIS Increment 2, can supply additional spatial coverage not currently included in the N-AIS Increment 1, which can provide an enhanced capability for monitoring shipping and improving managem- ent of coastal ship traffic and response to pollution incidents. These methods will not only improve response time, but reduce cost of response as well

A capacity study for vessel traffic using automatic identification system data

In this study, we created a simulation model to assess the overall impact of implementing a one-way traffic policy due to construction works. The inputs of the simulation model are found by performing statistical analysis on data from the Automatic Identification System (AIS). The aim of this study is twofold: (a) map the vessel traffic during the reference period and (b) analyse the congestion for the new traffic conditions. We use a non-homogeneous Poisson process with piecewise linear intensity to model the arrival process. For scenarios with varying arrival intensities, we compare the vessels' waiting times as well as the maximum queue lengths. The latter is important for upstream traffic since there are space constraints