Using Automatic Identification System Data in Vessel Route Prediction and Seaport Operations

In this paper, the authors perform a comprehensive literature review on the use of data obtained from the Automatic Identification System, with an emphasis on vessel route prediction and seaport operations. The usage of Automatic Identification System vessel’s position data in the vessel route prediction and seaport operations has been analyzed, to prove that Automatic Identification System data has a large potential to improve the efficiency of maritime transport. The authors concluded that proper vessel route prediction and route planning can improve voyage safety and reduce unnecessary costs. Furthermore, AIS can provide port authorities with early warnings, allowing them to take preemptive action to avoid possible congestions and unnecessary costs

Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms

Supply chain disruptions are expected to significantly increase over the next decades. In particular, delay of container vessels is likely to escalate due to rising congestion from continued growth of container shipping and higher frequency of extreme weather events. Predicting these delays could result in significant cost savings from optimizing operations. Both academic research and container shipping industry, however, lack analytical solutions to predict delay. To increase transparency on delay, we develop a prediction model based on 315 explanatory variables, 10 regression models, and 7 classification models. Using machine learning algorithms, we obtain best results for neural network and support vector machine with a prediction accuracy of 77 percent compared to only 59 percent of a naive baseline model. Various shipping players including sender, carrier, terminal operator, and receiver benefit from the easy-to-use prediction model to optimize operations such as buffers in schedules and the selection of ports and routes

Port waiting time for oil tankers : Leveraging AIS data to predict port waiting time using machine learning

This master's thesis investigates the predictability of waiting times at crude oil ports using Automatic Identification System (AIS) data and machine learning. Focusing on the wet bulk market, specifically four congested Middle Eastern Gulf ports, we aimed to answer: 11Can the waiting times in crude oil ports be predicted based on AIS data? 11. In this thesis clustering algorithms with novel modifications are utilized to establish berth and anchorage polygons. These polygons form the basis for a spatial matching of AIS data that is used to generate event logs. A cross-sectional data set is derived from the event logs which in turn is the basis for extracting features used in five different machine learning models. The findings show that AIS-derived features have predictive power on waiting times, with vessel composition within ports and port dynamics being significant factors. These insights hold practical implications for ship owners and academics alike, enhancing vessel economics through speed adjustments and facilitating further research within the maritime domain. The thesis also proposes further research areas, including methodology refinement within polygon generation, event log generation and waiting time prediction.nhhma

Using Machine Learning to Predict Port Congestion : A study of the port of Paranaguá

Being able to accurately predict future levels of port congestion is of great value to both port and ship operators. However, such a prediction tool is currently not available. In this thesis, a Long Short-Term Memory Recurrent Neural Network is built to fulfill this need. The prediction model uses information mined from Automatic Identification Systems (AIS) data, vessel characteristics, weather data, and commodity price data as input variables to predict the future level of congestion in the port of Paranaguå, Brazil. All data used in this study are publicly available. The predictions of the proposed model are shown to be promising with a satisfactory level of accuracy. The conclusion and evaluation of the presented model are that it serves its purpose and fulfills its objective within the constraints set by the authors and its inherent limitations.nhhma

DBSCAN algoritmin hyperparametri optimisointi käyttäen uudenlaista geneettiseen algoritmiin perustuvaa menetelmää

Ship traffic is a major source of global greenhouse gas emissions, and the pressure on the maritime industry to lower its carbon footprint is constantly growing. One easy way for ships to lower their emissions would be to lower their sailing speed. The global ship traffic has for ages followed a practice called "sail fast, then wait", which means that ships try to reach their destination in the fastest possible time regardless and then wait at an anchorage near the harbor for a mooring place to become available. This method is easy to execute logistically, but it does not optimize the sailing speeds to take into account the emissions. An alternative tactic would be to calculate traffic patterns at the destination and use this information to plan the voyage so that the time at anchorage is minimized. This would allow ships to sail at lower speeds without compromising the total length of the journey. To create a model to schedule arrivals at ports, traffic patterns need to be formed on how ships interact with port infrastructure. However, port infrastructure is not widely available in an easy-to-use form. This makes it difficult to develop models that are capable of predicting traffic patterns. However, ship voyage information is readily available from commercial Automatic Information System (AIS) data. In this thesis, I present a novel implementation, which extracts information on the port infrastructure from AIS data using the DBSCAN clustering algorithm. In addition to clustering the AIS data, the implementation presented in this thesis uses a novel optimization method to search for optimal hyperparameters for the DBSCAN algorithm. The optimization process evaluates possible solutions using cluster validity indices (CVI), which are metrics that represent the goodness of clustering. A comparison with different CVIs is done to narrow down the most effective way to cluster AIS data to find information on port infrastructure

Risk factors and navigation accidents: A historical analysis comparing accident-free and accident-prone vessels using indicators from AIS data and vessel databases

This paper presents the results of an explorative analysis aiming to identify indicators and factors associated with navigation accidents (groundings and collisions). The analysis compares cargo vessels with at least one registered navigation accident (grounding or collision) within Norwegian waters with those that have none, in the period 2010–2019. The comparison is made using data based on automatic identification system (AIS) satellite data in combination with information from IHS Fairplay, to construct indicators that reflect different characteristics of the vessels. Hallmarks of vessels involved in navigation accidents have been identified using bivariate and multivariate statistical analysis. The multivariate model was a strong predictor of vessels' accident involvement with 44% of the variance explained. Indicators that predicted reported navigation accidents included: (1) vessel type, (2) higher age, (3) smaller size, (4) longer distance sailed, (5) higher average speed, (6) flying Norwegian flag, (7) gray or black Tokyo MoU rating, and 8) not on US Coast Guard target list. The results are discussed relative to their potential causes as well as limits and practical applications. The study shows the promising potential of utilizing AIS data combined with various data sets to obtain knowledge on risk factors and risk indicators.publishedVersio