Study of narrow waterways congestion based on automatic identification system (AIS) data: A case study of Houston Ship Channel

Modeling is a promising approach to understand and predict the safety and efficiency of maritime traffic in ports and waterways. Different types of models have been developed over the years. Nevertheless, several important scientific challenges still remain. For instance, few models consider vessel behavior in ports and waterways under the influence of internal factors including vessel type and size, and external factors, such as wind and visibility. More data and research are needed to understand the influence of internal and external factors on vessel behavior including speed, course and path in ports and waterways; more research is also needed to explore human behavior of the bridge team for vessel maneuvering in ports and waterways. To address the needs listed, this thesis focuses on analyzing the influence of wind, visibility, current and vessel encounters on vessel speed, course and path using Automatic Identification System (AIS) data. Based on this analysis a new maritime traffic model has been developed that considers both internal and external factors, and aims to better predict the individual vessel behavior. The model can be used to provide data for the safety and efficiency assessment of vessel traffic in ports and inland waterways. In the last decades, the AIS system, which is an onboard autonomous and continuous broadcast system that transmits vessel data between nearby vessels and shore stations, has been developed. This is used now by almost all vessels. Therefore, AIS data, including vessel speed, course and path, can serve as a valuable data source to investigate vessel behavior. In this thesis, AIS data from a part of the port of Rotterdam is analyzed to investigate influences of different factors, such as vessel size and type, external conditions and vessel encounters, on vessel behavior. Firstly, vessels are distinguished into influenced and unhindered vessels based on certain thresholds that we obtained from the AIS data. The influenced vessel behavior is compared with the behavior of unhindered vessels, which are not influenced by other vessels or strong external influences of wind, visibility and current. The analysis provides evidence showing that the vessel behavior including vessel speed, course and path is influenced by various factors. Ship speed and path is influenced by internal factors (including vessel type, size, waterway geometry and navigation direction) and external factors (including wind, visibility, current, overtaking and head-on encounters), while ship course is only influenced by overtaking and head-on encounters. It can also be concluded that the AIS data is a useful source to get insights into vessel behavior.

Study on U-turn behavior of vessels in narrow waterways based on AIS data

Improving maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data

The maritime Automatic Identification System (AIS) data is obtained from many different terrestrial and satellite sources. AIS data enables safety, security, environmental protection and the economic efficiency of the maritime sector. The quality of AIS receivers is not controlled in the same manner as AIS transmitters. This has led to a situation where AIS data is not as clean as it should/could be. Added to this is the lack of accuracy and standards in entering the voyage data by the mariners such as next port of call into the AIS equipment installed on vessels. By using analytics IMIS Global Limited has been able to process the AIS data stream to eliminate a large portion of the faulty data. This has allowed the resultant AIS data to be used for more accurate detailed analysis such as the long-term vessel track, port arrival events and port departure events. New data that is derived from processing AIS data has enhanced the information available to maritime authorities enabling a significant increase in safety, security, environmental protection and economic growth. The next generation of maritime data communications technology being based AIS. This is known as the VHF Data Exchange System (VDES) and this technology now enables further opportunities. The value from the large volumes of AIS data is extracted by visual, streaming, historical and prescriptive data analytics. The datAcron project is showing the way with regards to the processing and use of AIS and resultant trajectory data.

Resiliency measurement is a great tool for evaluating system performance and proposing solutions to prevent damage and to recover from disruptive events. This study proposes an analytic approach to quantify narrow waterway systems’ resiliency during disasters. First, metrics are introduced to quantify the resiliency before, during, and after a disruption. The existing Key Performance Indicators (KPIs) for Maritime Transportation Systems (MTS) are examined, and two metrics, 1) the number of inbound and outbound vessels and 2) Total Stopped Vessel-Hours, are selected to measure the resiliency of a waterway system. Second, a heuristic method is developed to derive the KPIs from the Automatic Identification System (AIS) data. Finally, the proposed methodology is performed for the Houston Ship Channel (HSC) AIS data before, during, and after Hurricane Harvey, in August 2017. The results are presented for the entire channel and highlight useful information about the performance of individual docks, terminals, and waterway zones within HSC. This study helps decision-makers identify the weaknesses and potential bottlenecks in a waterway confronting a disruption and come up with remedies.

Abstract. The high-resolution ship emission inventory serves as a crucial dataset for various disciplines including atmospheric science, marine science, and environmental management. Here, we present a global high-spatiotemporal-resolution ship emission inventory at a resolution of 0.1° × 0.1° for the years 2013 and 2016–2021, generated by the state-of-the-art Shipping Emission Inventory Model (SEIMv2.2). Initially, the annual 30 billion Automatic Identification System (AIS) data underwent extensive cleaning to ensure data validity and accuracy in temporal and spatial distribution. Subsequently, integrating real-time vessel positions and speeds from AIS data with static technical parameters, emission factors, and other computational parameters, SEIM simulated ship emissions on a ship-by-ship, signal-by-signal basis. Finally, the results were aggregated and analyzed. In 2021, the ship activity dataset established based on AIS data covered 109 300 vessels globally (101 400 vessels reported by the United Nations Conference on Trade and Development). Concerning the major air pollutants and greenhouse gases, global ships emitted 847.2×106 t of CO2, 2.3×106 t of SO2, 16.1×106 t of NOx, 791.2 kt of CO, 737.3 kt of HC (hydrocarbon), 415.5 kt of primary PM2.5, 61.6 kt of BC (black carbon), 210.3 kt of CH4, and 45.1 kt of N2O in 2021, accounting for 3.2 % of SO2, 14.2 % of NOx, and 2.3 % of CO2 emissions from all global anthropogenic sources, based on the Community Emissions Data System (CEDS). Due to the implementation of fuel-switching policies, global ship emissions of SO2 and primary PM2.5 saw a significant reduction of 81.3 % and 76.5 % in 2021 compared to 2019, respectively. According to the inventory results, the composition of vessel types contributing to global ship emissions remained relatively stable through the years, with container ships consistently contributing ∼ 30 % of global ship emissions. Regarding vessel age distribution, the emission contribution of vessels built before 2000 (without Tier standards) has been declining, dropping to 10.2 % in 2021, suggesting that even a complete phase-out of these vessels would have limited potential for reducing NOx emissions in the short term. On the other hand, the emission contribution of vessels built after 2016 (meeting Tier III standard) kept increasing, reaching 13.3 % in 2021. Temporally, global ship emissions exhibited minimal daily fluctuations. Spatially, high-resolution emission characteristics of different vessel types were delineated. Patterns of ship emission contributions by different types of vessels vary among maritime regions, with container ships predominant in the North and South Pacific, bulk carriers predominant in the South Atlantic, and oil tankers prevalent in the Arabian Sea. The distribution characteristics of ship emissions and intensity also vary significantly across different maritime regions. Our dataset, which is accessible at https://doi.org/10.5281/zenodo.10869014 (Wen et al., 2024), provides a daily breakdown by vessel type and age; it is available for broad research purposes, and it will provide a solid data foundation for fine-scale scientific research and shipping emission mitigation.

AIS (Automatic Identification System) data received from moving vessels over an area of interest can be of very much interest for deriving maritime trajectory patterns. In this paper, a novel approach to extract course patterns from AIS data of vessels is presented. From machine learning and natural language processing principles, a topic model might be used for extracting implicit patterns underlying massive and unstructured collection of incoming data. To apply topic model to AIS data, PQk-means vector quantization to convert AIS data record to code documents is introduced. Then, a topic model is applied to extract course patterns from AIS data. In fact, courses, not only encompasses trajectory locations, but also headings and speeds, are recognized by the proposed algorithm. The performance of PQk-means is evaluated using the relative root mean square error and elapsed time. The potential of the approach is illustrated by a series of experimental results derived from practical AIS data set in a region of North West France.

We investigated ship navigation records known as Automatic Identification System (AIS) data near the source region of the 2011 Tohoku, Japan, tsunami. The AIS data of 16 ships in the offshore navigation could be compiled by about 40 min after the tsunami generation. Most of the AIS data showed notable deviation of the ship heading from the course over ground during the tsunami passage. There was good agreement in terms of amplitude/phase between the ship velocity and the simulated tsunami velocity in the direction normal to the ship heading. An equation of motion due to wave drag and inertia forces was examined for an offshore movable floating body. We explain that the ship movement in the direction normal to the heading immediately responds to the tsunami current, and relative velocity between the ship and the tsunami current asymptotically become zero. This indicates the movement velocity of navigating ships in the direction normal to the heading derived from AIS data will work as an offshore tsunami current meter. We examined the AIS data during the 2011 Tohoku tsunami and showed these data could be useful for tsunami source estimation and forecast. The AIS data in the current framework will possibly be a crowd-sourced tool for monitoring offshore tsunami current and tsunami forecast.

Coastal, port, and waterway projects often require an understanding of the waterborne traffic in the site vicinity. Knowledge of what types of vessels transit near a project site, along with the vessel speeds and typical transit times/paths, can be valuable information to an engineer. Often the best available information for vessel traffic can be obtained from Automatic Identification System (AIS) data. AIS data includes information about the vessel type, position, course, and speed (IMO, 2014). Historic AIS data is available from a variety of free and commercial sources. Inside the United States, a large quantity of AIS data is available freely to the public from the United States’ Coast Guard datasets. AIS data can be summarized in a variety of tabular and graphic formats. For spatial planning and visualization, an intuitive format for communicating vessel traffic to non-technical audiences is a vessel density map. Straightforward methods are available (BOEM/NOAA, 2015) for producing vessel density maps in relatively open water and away from sharp channel bends. This paper addresses challenges with preparing vessel maps in areas with narrow channels and around bends.

Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45–99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints.

In the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

Maritime situational awareness at over-the-horizon (OTH) distances in exclusive economic zones can be achieved by deploying networks of high-frequency OTH radars (HF-OTHR) in coastal countries along with exploiting automatic identification system (AIS) data. In some regions the reception of AIS messages can be unreliable and with high latency. This leads to difficulties in properly associating AIS data to OTHR tracks. Long history records about the previous whereabouts of vessels based on both OTHR tracks and AIS data can be maintained in order to increase the chances of fusion. If the quantity of data increases significantly, data cleaning can be done in order to minimize system requirements. This process is performed prior to fusing AIS data and observed OTHR tracks. In this paper, we use fuzzy functional dependencies (FFDs) in the context of data fusion from AIS and OTHR sources. The fuzzy logic approach has been shown to be a promising tool for handling data uncertainty from different sensors. The proposed method is experimentally evaluated for fusing AIS data and the target tracks provided by the OTHR installed in the Gulf of Guinea.

In this paper, we present an automated process for detecting the anomaly in Automatic Identification System (AIS) data. Machine learning approaches have been employed to automatically detect anomalies in the AIS data. The opensource AIS data is been used to evaluate the performance of the proposed approach. Supervised machine learning approaches like K Nearest Neighbor, Random Forest, Support Vector Machine classifier is employed to predict the anomalies in the AIS data. The AIS data does not contain the ground truth labels and supervised learning algorithms need labelling data, to address this issue, we employed an unsupervised approach to label the data based on the prior information and characteristics of the AIS data. The labelled data is then used to train the supervised machine learning models. The proposed approach with support vector machine classifier has classified the AIS data into normal and anomaly with an accuracy of 96.5%.KeywordsAISMachine learningCourse Over Ground (COG)International Maritime Organization (IMO)Maritime Mobile Service Identity (MMSI)Ship attacksSpeed Over Ground (SOG)

Reconstructing trajectories and extracting shipping routes between ports based on AIS data