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

Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Agent-based Modelling and Big Data: Applications for Maritime Traffic Analysis

Agent based modeling (ABM) is a powerful tool for examining complex systems in many scientific applications, including maritime transport systems. Growing demands for freight transport and increased industry emphasis on reducing environmental impacts have heightened the focus on vessel and port efficiency. This research aimed to create a maritime route planning model to simulate vessel movement in all waterways. The goal of the ship routing model developed in this research was to develop a simulation tool capable of reproducing real world shipping routes useful for navigation planning, with emphasis on port scheduling and potential application for further use and exploration. A modified breadth-first search algorithm was implemented as a NetLogo ABM in this research. With increasing volumes of ship location monitoring data, new approaches are now possible for examining performance-based metrics and to improve simulations with more precise verification and analysis. A Satellite Automatic Identification System dataset with over 500,000 vessel logs travelling across the Pacific Ocean and into the Port of Metro Vancouver was used as the focal area for model development and validation in this study. Automatic identification system (AIS) is the global standard for maritime navigation and traffic management, and data derived from AIS messages can be used for calibrating simulation model scenarios. In this analysis, the results examined how changes in simulation parameters alter route choice behaviour and how effective large AIS datasets are for validating and calibrating model results. Using large AIS datasets, model results can be quantified to examine how closely they resemble real-time vessels in the same region. Heatmaps provide a data visualization tool that effectively uses large data sets and calculates how closely model results resemble AIS data from the same region. In the case of PMV, the Maritime Ship Routing Model (MSRM) was able to replicate path likeness with a high level of accuracy, generating realistic navigation paths between the many islands on the eastern side of southern Vancouver Island, B.C., a busy marine traffic region and sensitive ecological area. This research highlights the use of ABM as a powerful, user-friendly tool for developing maritime shipping models useful for port scheduling and route analysis. The results of this study emphasize the use of large data sets that are applicable, clean, and reliable as a crucial source for validating and calibrating the MSRM

Algorithms for learning from spatial and mobility data

Data from the numerous mobile devices, location-based applications, and collection sensors used currently can provide important insights about human and natural processes. These insights can inform decision making in designing and optimising in frastructure such as transportation or energy. However, extracting patterns related to spatial properties is challenging due to the large quantity of the data produced and the complexity of the processes it describes. We propose scalable, multi-resolution approximation and heuristic algorithms that make use of spatial proximity properties to solve fundamental data mining and optimisation problems with a better running time and accuracy. We observe that abstracting from individual data points and working with units of neighbouring points based on various measures on similarity, improves computational efficiency and diminishes the effects of noise and overfitting. We consider applications in: mobility data compression, transit network planning, and solar power output prediction. Firstly, in order to understand transportation needs, it is essential to have efficient ways to represent large amounts of travel data. In analysing spatial trajectories (for example taxis travelling in a city), one of the main challenges is computing distances between trajectories efficiently; due to their size and complexity this task is computationally expensive. We build data structures and algorithms to sketch trajectory data that make queries such as distance computation, nearest neighbour search and clustering, which are key to finding mobility patterns, more computationally efficient. We use locality sensitive hashing, a technique that associates similar objects to the same hash. Secondly, to build efficient infrastructure it is necessary to satisfy travel demand by placing resources optimally. This is difficult due to external constraints (such as limits on budget) and the complexity of existing road networks that allow for a large number of candidate locations. For this purpose, we present heuristic algorithms for efficient transit network design with a case study on cycling lane placement. The heuristic is based on a new type of clustering by projection, that is both computationally efficient and gives good results in practice. Lastly, we devise a novel method to forecast solar power output based on numerical weather predictions, clear sky predictions and persistence data. The ensemble of a multivariate linear regression model, support vector machines model, and an artificial neural network gives more accurate predictions than any of the individual models. Analysing the performance of the models in a suite of frameworks reveals that building separate models for each self-contained area based on weather patterns gives a better accuracy than a single model that predicts the total. The ensemble can be further improved by giving performance-based weights to the individual models. This suggests that the models identify different patterns in the data, which motivated the choice of an ensemble architecture

Integrated vehicle dispatching for container terminal

Ph.DDOCTOR OF PHILOSOPH

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

Maritime modular anomaly detection framework

Detecting maritime anomalies is an extremely important task for maritime agencies around the globe. With the number of vessels at seas growing exponentially, the need for novel automated methods to support them with their routines and upgrade existing technologies is undeniable. MARISA, the Maritime Integrated Surveillance Awareness project, aims at fostering collaboration between 22 governmental organisations and enhance the reaction and decision-making capabilities of the maritime authorities. This work describes our contributions to the development of MARISA’s common toolkit for the detection of maritime anomalies. These efforts, as part of a Masters’ dissertation, lead to the development of the Modular Anomaly Detection Framework, MAD-F, a full data pipe-line which applies efficient and reliable routines to raw vessel navigational data in order to output potential maritime vessel anomalies. The anomalies considered for this work were defined by the experts from various maritime institutions, through MARISA, and allowed us to implement solutions given the real needs in the industry. The MADF functionalities will be validated through actual real maritime exercises. In its current state, we believe that the MAD-F is able to support maritime agencies and be integrated into their legacy systems.Detetar anomalias marítimas é uma tarefa extremamente importante para agências marítimas á escala mundial. Com o número de embarcações em mar crescendo exponencial, a necessidade de desenvolver novas rotinas de suporte ás suas atividades e de atualizar as tecnologias existentes é inegável. MARISA, o projeto de Conscientização da Vigilância Integrada Marítima, visa fomentar a colaboração entre 22 organizações governamentais e melhorar as capacidades de reação e tomada de decisões das autoridades marítimas. Este trabalho descreve as nossas contribuições para o desenvolvimento do toolkit global MARISA, que tem como âmbito a deteção de anomalias marítimas. Estas contribuições servem como parte do desenvolvimento da Modular Anomaly Detection Framework (MAD-F), que serve como um data-pipeline completo que transforma dados de embarcações não estruturados em potenciais anomalias, através do uso de métodos eficientes para tal. As anomalias consideradas para este trabalho foram definidas através do projeto MARISA por especialistas marítimos, e permitiram-nos trabalhar em necessidades reais e atuais do sector. As funcionalidades desenvolvidas serão validadas através de exercícios marítimos reias. No estado atual do MAD-F acreditamos que este será capaz de apoiar agências marítimas, e de posteriormente ser integrado nos sistemas dos mesmos