Resilience-Focused Analysis of the United States Maritime Transportation System Using Automatic Identification System Data

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

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.

The USACE, St. Louis District, is responsible for maintaining navigation channels along with multiple lock and dam structures on the Mississippi River, a vital inland waterway that carries millions of tons of commodities every year. Understanding commercial vessel traffic patterns is fundamental to informing decisions about construction projects and to efforts to improve communication to mariners. Automatic Identification System (AIS) data provides time-stamped and geo-referenced vessel position reports for most commercial vessels operating in the District’s area of interest. This paper describes how AIS data has been successfully used by St. Louis District waterway managers to (1) prevent conflicts with the navigation industry by revealing active fleeting areas that were under consideration for the construction of river training structures; and (2) identify changes in vessel approaches to a lock structure under different river flow conditions, providing operational information that could be used in future navigation alerts to mariners. This paper concludes with a list of suggested best practices for waterways managers who want to start, or expand, their use of AIS 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.

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

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 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)

A novel method for restoring the trajectory of the inland waterway ship by using AIS data

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.

This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations.

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

This paper proposes a simulation-based method to estimate collision risk for a ship operating in a two-lane canal. According to rule 9 of the Colreg-72 navigation rules, in a narrow canal, a vessel shall keep as near to the wall that lies on its starboard side. However, a busy harbor entered through a narrow canal still presents impact hazards. Certain conditions in a two-lane canal, such as a head-on situation in the straight part of the canal during an overtaking maneuver and large curvature of a turning maneuver in the bend part of the canal, could lead to accidents. In the first condition, the ship alters its own course to the port side to overtake another ship in the same lane but the course altered is too large and hits the wall of the canal. In the second condition, the target ship may take an excessively large turn on the bend part of the canal, causing collision with the ship on the opposite lane. Collision risk is represented as the risk of damage to the ship structure and includes the probability of impact accident and severity of structural damage. Predictions of collision probabilities in a two-lane canal have been developed based on a simulation of ship maneuvering using a mathematical maneuvering group (MMG) model and automatic identification system (AIS) data. First, the propeller revolution and rudder angle of the subject ship are simulated to determine safe trajectories in both parts of the canal. Second, impact accidents are simulated for both conditions. The ship’s speed, and current and wind velocity are randomly simulated based on the distribution of the AIS and environment data for the research area. The structural consequences of the impact accident are measured as collision energy losses, based on the external dynamics of ship collision. The research area of the two-lane canal is located at the Madura Strait between the Java and Madura islands in East Java of Indonesia, as shown by the red line in Figure 1. A project for developing a new port and dredging a new two-lane canal to facilitate an increase in the number of ship calls is currently underway in the research area. Figure 1 shows the ships’ trajectories plotted using the AIS data as on January 1, 2011. The trajectories are mostly seen to be coming out of the canal, confirming that it is shallow and needs to be dredged.

This paper aims to analyze the North Brazil Current (NBC) rings during the initial 5 months of 2020 using surface currents derived from Automatic Identification System (AIS) data in comparison with altimetry-based Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) current fields. The region of NBC rings is characterized by relatively high marine traffic, facilitating an accurate current estimation. Our investigation primarily focused on a brief period coinciding with intensive in situ measurements (EUREC4A-OA experiment). The Angular Momentum Eddy Detection and tracking Algorithm (AMEDA) detection algorithm was then employed to detect and track eddies in both fields. Subsequently, a particular NBC ring present in the region in January and February 2020 was examined. The comparison demonstrated that AIS data exhibited the precision and resolution necessary to effectively identify the NBC rings and smaller surrounding eddies, aligning well with other datasets such as in situ measurements, sea surface temperature (SST), and sea surface salinity (SSS) data. Moreover, we established that AIS data yielded accurate regional velocity fields, as evidenced by an analysis of energy spectra. Furthermore, our analysis revealed that AIS data captured aspects of eddy–eddy interactions which were not adequately depicted in AVISO fields.

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.

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.