A data mining approach is presented for probabilistic characterization of maritime traffic and anomaly detection. The approach automatically groups historical traffic data provided by the Automatic ...Identification System in terms of ship types, sizes, final destinations and other characteristics that influence the maritime traffic patterns off the continental coast of Portugal. The approach consists of identifying relevant waypoints along a route where significant changes in the ships’ navigational behaviour are observed, such as changes in heading, using trajectory compression and clustering algorithms. This provides a vector-based representation of the ship routes consisting of straight legs and connecting turning sections that facilitates route probabilistic characterization and anomaly detection. The maritime traffic is characterized probabilistically at the identified route legs and waypoints in terms of lateral distribution of the trajectories and speed profile, which allows the characterization of the typical behaviour of a group of similar ships along a particular route. In the proposed approach heading changes are automatically detected using the Douglas and Peucker algorithm and clustered by the density-based spatial clustering of applications with noise algorithm. The proposed method is applied to the characterization of southbound maritime traffic from the traffic separation scheme off Cape Roca to the ports of Lisbon, Setúbal and Sines. Finally, an example of ship trajectory anomaly detection based on the developed maritime traffic probabilistic models is provided.
•A data mining approach is presented for probabilistic characterization of the maritime traffic off the coast of Portugal.•The approach automatically groups historical traffic data in terms of ship types, sizes, final destinations.•The approach is identifying relevant waypoints where significant changes on the ships' dynamic behaviour are observed.•An example of ship trajectory anomaly detection based on the developed maritime traffic probabilistic models is provided.
The availability of automatic identification system (AIS) data for tracking vessels has paved the way for improvements in maritime safety and efficiency. However, one of the main challenges in using ...AIS data is often the low quality of the data. Practically, AIS-based trajectory data of vessels are available at irregular time intervals; consequently, large temporal gaps often exist in the historical AIS data. Meanwhile, certain tasks such as waypoint detection using historical data, which involves finding locations along the trajectory where the vessel changes its course (and possibly speed, acceleration, etc.), require AIS messages with a high temporal resolution. High-resolution AIS data are especially required for waypoint detection in critical areas where vessels maneuver carefully because of, e.g., narrow pathways or the presence of islands. One possible solution to address the problem of insufficient AIS data in vessel trajectories is interpolation. In this paper, we address the problem of detecting waypoints in a single representative trajectory with insufficient data using various interpolation-based methods. To this end, a two-step approach is proposed, in which the trajectories are first interpolated, and then the waypoint detection method is applied to the merged trajectory containing both interpolated and observed AIS messages. The numerical results demonstrate the effectiveness of exploiting various interpolation methods for waypoint detection. Moreover, the results of the numerical experiments show that the proposed methodology is effective for waypoint detection in envisaged settings with insufficient data, and outperforms the competing algorithm.
This study looked at how Jordanian Islamic Banks' efficient performance was impacted by the quality of AIS outputs (relevance, procedures and instructions, credibility, timeliness, and feedback ...value). This is a cross-sectional survey with a quantitative research approach. Managers and officials of the finance department at "Jordanian Islamic Banks" served as the responders, and a total of 150 valid questionnaires were obtained from them. PLS-SEM (PLS 4.0) was used to evaluate the data. It was discovered that the performance of Jordanian Islamic Banks is significantly and favorably impacted by the quality of AIS as well as AIS outputs, including Relevance, Procedures and instructions, Credibility, Timeliness, and Feedback Value. The researcher came to certain conclusions and made some suggestions for more investigation.
An increasing number of marine conservation initiatives rely on data from Automatic Identification System (AIS) to inform marine vessel traffic associated impact assessments and mitigation policy. ...However, a considerable proportion of vessel traffic is not captured by AIS in many regions of the world. Here we introduce two complementary techniques for collecting traffic data in the Canadian Salish Sea that rely on optical imagery. Vessel data pulled from imagery captured using a shore-based autonomous camera system (“Photobot”) were used for temporal analyses, and data from imagery collected by the National Aerial Surveillance Program (NASP) were used for spatial analyses. The photobot imagery captured vessel passages through Boundary Pass every minute (Jan–Dec 2017), and NASP data collection occurred opportunistically across most of the Canadian Salish Sea (2017–2018). Based on photobot imagery data, we found that up to 72 % of total vessel passages through Boundary Pass were not broadcasting AIS, and in some vessel categories this proportion was much higher (i.e., 96 %). We fit negative binomial General Linearized Models to our photobot data and found a strong seasonal variation in non-AIS, and a weekend/weekday component that also varied by season (interaction term p < 0.0001). Non-AIS traffic was much higher during the summer (Apr–Sep) and during the weekend (Sat-Sun), reflecting patterns in recreational vessel traffic not obligated to broadcast AIS. Negative binomial General Additive Models based on the NASP data revealed strong spatial associations with distance from shore (up to 10 km) and non-AIS vessel traffic for both summer and winter seasons. There were also associations between non-AIS vessels and marina and anchorage densities, particularly during the winter, which again reflect seasonal recreational vessel traffic patterns. Overall, our GAMs explained 20–37 % of all vessel traffic during the summer and winter, and highlighted subregions where vessel traffic is under represented by AIS.
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•Automatic Identification System (AIS) increasingly used for marine conservation.•Introduce 2 novel vessel traffic data collection systems complementary to AIS•Quantify uncertainty using AIS to represent marine vessel associated threats•Uncertainty quantified in space and time
We present a comprehensive global shipping emission inventory and the global activities of ships for the year 2015. The emissions were evaluated using the Ship Traffic Emission Assessment Model ...(STEAM3), which uses Automatic Identification System data to describe the traffic activities of ships. We have improved the model regarding (i) the evaluation of the missing technical specifications of ships, and (ii) the treatment of shipping activities in case of sparse satellite AIS-data. We have developed a model for the collection and processing of available information on the technical specifications, using data assimilation techniques. We have also developed a path regeneration model that constructs, whenever necessary, the detailed geometry of the ship routes. The presented results for fuel consumption were qualitatively in agreement both with those in the 3rd Greenhouse Gas Study of the International Maritime Organisation and those reported by the International Energy Agency. We have also presented high-resolution global spatial distributions of the shipping emissions of NOx, CO2, SO2 and PM2.5. The emissions were also analysed in terms of selected sea areas, ship categories, the sizes of ships and flag states. The emission datasets provided by this study are available upon request; the datasets produced by the model can be utilized as input data for air quality modelling on a global scale, including the full temporal and spatial variation of shipping emissions for the first time. Dispersion modelling using this inventory as input can be used to assess the impacts of various emission abatement scenarios. The emission computation methods presented in this paper could also be used, e.g., to provide annual updates of the global ship emissions.
•A model (STEAM3) for the assessment of global shipping emissions is presented.•The modelling is based on ship activities given by AIS, for more than 300,000 ships.•A route generation algorithm is used to handle large gaps in the AIS-data.•Data-assimilation is used to assign physically realistic properties for each ship.•Results for global shipping emissions have been analysed and presented for 2015.
Identifying factors defining the effectiveness of integrated AIS in the Enterprise Resource Planning (ERP) environment is really a challenging task. In our research, the effectiveness of integrated ...AIS in ERP is presented in the form of a Balanced Scorecard (BSC) model. This study analyzes data collected from 178 Vietnamese garment companies with AIS in an ERP environment. Then, Cronbach’s Alpha test and exploratory factor analysis (EFA) are conducted to assess the reliability of variables. The result identifies 28 variables from Vietnamese garment companies’ managers view grouped into 4-dimensional constructs of the BSC model that define the effectiveness of integrated AIS in an ERP environment. The conclusion on garment companies’ AIS evaluation factors paves the way for future research on other Vietnamese industries’ AIS evaluation in an ERP environment.
The purpose of this study is to determine the factors affecting the application of accounting information systems (AIS) in small and medium enterprises (SMEs) in Vietnam. Drawing upon the ...Technology–Organization–Environment (TOE) theoretical framework, Diffusion of Innovations theory (DOI), and Resource-based theory (RBV), we proposed a research model to investigate the antecedents and influence of AIS usage in Vietnamese SMEs. This study used an online survey of individuals who work in Vietnamese SMEs for data collection. The result was assembled by applying the PLS-SEM model to test the proposed hypotheses based on 132 valid responses. First, the factors that have a significant impact on AIS usage are as follows: relative advantage; owner/manager commitment; and impact of COVID-19. Second, the research results also confirm that there is a positive relationship between AIS usage and AIS effectiveness; AIS performance has a positive impact on business performance. Research implications are to help business owners and leaders decide whether to use AIS to strengthen the company’s position and reduce the burden on departments, particularly the accounting department.
Aim: To investigate the association of small dense low-density lipoprotein cholesterol (sdLDL-C) and acute ischemic stroke (AIS) in terms of risk, severity, and outcomes. Prediction models were ...established to screen high-risk patients and predict prognosis of AIS patients. Methods: We enrolled in this study 355 AIS patients and 171 non-AIS controls. AIS was subtyped according to TOAST criteria, and the severity and outcomes of AIS were measured. Blood glucose and lipid profiles including total cholesterol, triglyceride, and lipoproteins were measured in all patients using automatic measure. Lipoprotein subfractions were detected by the Lipoprint LDL system. Results: As compared with the non-AIS control group, the AIS group had higher sdLDL-C levels. Pearson correlation analysis revealed that the sdLDL-C level and risk of AIS, especially non-cardioembolic stroke, were positively correlated. The area under the curve of sdLDL-C for AIS risk was 0.665, better than that of other lipids. Additionally, the sdLDL-C level was significantly correlated with AIS severity and bad outcomes. A logistic regression model for assessing the probability of AIS occurrence and a prognostic prediction model were established based on sdLDL-C and other variables. Conclusions: Elevated levels of sdLDL-C were associated with a higher prevalence of AIS, especially in non-cardioembolic stroke subtypes. After adjustment for other risk factors, sdLDL-C was found to be an independent risk factor for AIS. Also, sdLDL-C level was strongly associated with AIS severity and poor functional outcomes. Logistic regression models for AIS risk and prognosis prediction were established to help clinicians provide better prevention for high-risk subjects and monitor their prognosis.
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