[pt] Em ambientes dinâmicos, os modelos de dados tendem a ter desempenho insatisfatório uma vez que a distribuição subjacente dos dados muda. Este fenômeno é conhecido como Concept Drift. Em relação a este tema, muito esforço tem sido direcionado ao desenvolvimento de métodos capazes de detectar tais fenômenos com antecedência suficiente para que os modelos possam se adaptar. No entanto, explicar o que levou ao drift e entender suas consequências ao modelo têm sido pouco explorado pela academia. Tais informações podem mudar completamente a forma como adaptamos os modelos. Esta dissertação apresenta uma nova abordagem, chamada Detector de Drift Interpretável, que vai além da identificação de desvios nos dados. Ele aproveita a estrutura das árvores de decisão para prover um entendimento completo de um drift, ou seja, suas principais causas, as regiões afetadas do modelo e sua severidade. / [en] In a dynamic environment, models tend to perform poorly once the underlying distribution shifts. This phenomenon is known as Concept Drift. In the last decade, considerable research effort has been directed towards developing methods capable of detecting such phenomena early enough so that models can adapt. However, not so much consideration is given to explain the drift, and such information can completely change the handling and understanding of the underlying cause. This dissertation presents a novel approach, called Interpretable Drift Detector, that goes beyond identifying drifts in data. It harnesses decision trees’ structure to provide a thorough understanding of a drift, i.e., its principal causes, the affected regions of a tree model, and its severity. Moreover, besides all information it provides, our method also outperforms benchmark drift detection methods in terms of falsepositive rates and true-positive rates across several different datasets available in the literature.

Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. In this thesis, we explore the detection of corners, free kicks, and throw-in events by means of neural networks. Training a model to solve this task is not easy; the neural network needs to model the spatio-temporal interactions between different agents moving in a 2-dimensional space. We decided to address this problem using graph neural networks in combination with recurrent neural networks, which allow us to model respectively the spatial and temporal components of the data. Tracking the position of the ball is difficult, which makes the dataset noisy. In this thesis, we mainly work with a version of the dataset where the position of the ball has been manually corrected. However, to study how the noisy position of the ball affects the results we also train the models on the original data. The results show that detecting the corner and the throw-in is much easier than detecting the free kick. Moreover, the noisy position of the ball affects significantly the performance of the model. We conclude that to train the model on the original data it is necessary to use a much larger training set. Since the amount of training data for these events is limited, we also train the model on the more generic ball-dead-to-alive event, for which much more data is available, and we observe that by increasing the amount of training data the results can improve significantly. In this report, we also provide an in-depth discussion about all the challenges faced during the project and how different hyperparameters and design choices can affect the results. / Tracabs optiska spårningssystem gör det möjligt att spåra de 2-dimensionella banorna för spelare och boll under en fotbollsmatch. Med hjälp av dessa data är det möjligt att träna maskininlärningsmodeller för att identifiera händelser som inträffar under matchen. I denna avhandling utforskar vi upptäckten av hörnor, frisparkar och inkastningshändelser med hjälp av neurala nätverk. Att träna en modell för att lösa denna uppgift är inte lätt; det neurala nätverket behöver modellera de rums-temporala interaktionerna mellan olika agenter som rör sig i ett 2-dimensionellt rum. Vi bestämde oss för att ta itu med detta problem med hjälp av grafiska neurala nätverk i kombination med återkommande neurala nätverk, vilket gör att vi kan modellera de rumsliga respektive temporala komponenterna i datan. Det är svårt att spåra bollens position, vilket gör datauppsättningen bullrig. I detta examensarbete arbetar vi främst med en version av datamängden där bollens position har korrigerats manuellt. Men för att studera hur bollens bullriga position påverkar resultaten tränar vi också modellerna på originaldata. Resultaten visar att det är mycket lättare att upptäcka hörna och inkastet än att upptäcka frisparken. Dessutom påverkar bollens bullriga position avsevärt modellens prestanda. Vi drar slutsatsen att för att träna modellen på originaldata är det nödvändigt att använda en mycket större träningsuppsättning. Eftersom mängden träningsdata för dessa evenemang är begränsad, tränar vi också modellen på den mer generiska bollen död-till-levande-händelsen, för vilken mycket mer data finns tillgänglig, och vi observerar att genom att öka mängden träningsdata resultaten kan förbättras avsevärt. I denna rapport ger vi också en fördjupad diskussion om alla utmaningar som ställs inför under projektet och hur olika hyperparametrar och designval kan påverka resultaten.

<p>  </p> <p>Most new vehicles manufactured in the last two years are connected vehicles (CV) that transmit back to the original equipment manufacturer at near real-time fidelity. These CVs generate billions of data points on an hourly basis, which can provide valuable data to agencies to improve the overall mobility experience for users. However, with this growing scale of CV big data, stakeholders need efficient and scalable methodologies that allow agencies to draw actionable insights from this large-scale data for daily operational use. This dissertation presents a suite of applications, illustrated through case studies, that use CV data for assessing and managing mobility and safety on surface transportation systems.</p> <p>A systematic review of construction zone CV data and crashes on Indiana’s interstates for the calendar year 2019, found a strong correlation between crashes and hard-braking event data reported by CVs. Trajectory-level CV data analyzed for a construction zone on interstate 70 provided valuable insights into travel time and traffic signal performance impacts on the surrounding road network. An 11-state analysis of electric and hybrid vehicle usage in proximity to public charging stations highlighted regions under and overserved by charging infrastructure, providing quantitative support for infrastructure investment allocations informed by real-world usage trends. CV data were further leveraged to document route choice behavior during active freeway incidents providing stakeholders with a historical record of observed routing patterns to inform future alternate route planning strategies. CV trajectory data analysis facilitated the identification of trip chaining activities resulting in improved outlier curation and realistic estimation of travel time metrics.</p> <p>The overall contribution of this thesis is developing analytical big data procedures to process billions of CV data records to inform engineering and public policy investments in infrastructure capacity, highway safety improvements, and new EV infrastructure. These scalable and efficient analysis techniques proposed in this dissertation will help agencies at the federal, state and local levels in addition to private sector stakeholders in assessing transportation system performance at-scale and enable informed data-driven decision making.</p>

Le suivi, le contrôle et la surveillance (MCS) des pêches marines sont des problèmes essentiels pour la gestion durable des ressources halieutiques. Dans cette thèse, nous étudions le suivi spatial des activités des navires de pêche en utilisant les données de trajectoire du système de surveillance des navires (VMS) dans le cadre du projet INDESO (2013-2017). Notre objectif général est de développer une chaîne de traitement des données VMS afin de: i) effectuer un suivi de l'effort de pêche des flottilles de palangriers indonésiens, ii) détecter les activités de pêche illégales et évaluer leur importance. L'approche proposée repose sur des modèles de mélange gaussien (GMM) et les modèles de Markov cachés (HMM), en vue d'identifier les comportements élémentaires des navires de pêche, tels que les voyages, la recherche et les activités de pêche, dans un cadre non supervisé. Nous considérons différentes paramétrisations de ces modèles avec une étude particulière des palangriers indonésiens, pour lesquels nous pouvons bénéficier de données d'observateurs embarqués afin de procéder à une évaluation quantitative des modèles proposés et testés.Nous exploitons ensuite ces modèles statistiques pour deux objectifs différents: a) la discrimination des différents flottilles de pêche à partir des trajectoires des navires de pêche et l'application à la détection et à l'évaluation des activités de pêche illégale, b) l'évaluation d'un effort de pêche spatialisé à partir des données VMS. Nous obtenons de très bons taux de reconnaissance (environ 97%) pour la première tâche et nos expériences soutiennent le potentiel d'une exploration opérationnelle de l'approche proposée. En raison du nombre limité de données d'observateurs embarqués, seules des analyses préliminaires on pu être effectuées pour l'estimation de l'effort de pêche à partir des données VMS. Au-delà des développements méthodologiques potentiels, cette thèse met l'accent sur l'importance de la qualité de données d'observation en mer représentatives pour développer davantage l'exploitation des données VMS tant pour la recherche que pour les questions opérationnelles. / Monitoring, control and surveillance (MCS) of marine fisheries are critical issues for the sustainable management of marine fisheries. In this thesis we investigate the space-based monitoring of fishing vessel activities using Vessel Monitoring System (VMS) trajectory data in the context of INDESO project (2013-2017). Our general objective is to develop a processing chain of VMS data in order to: i) perform a follow-up of the fishing effort of the Indonesian longline fleets, ii) detect illegal fishing activities and assess their importance. The proposed approach relies on classical latent class models, namely Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), with a view to identifying elementary fishing vessel behaviors, such as travelling, searching and fishing activities, in a unsupervised framework. Following state-of-the-art approaches, we consider different parameterizations of these models with a specific focus on Indonesian longliners, for which we can benefit from at-sea observers¿ data to proceed to a quantitative evaluation. We then exploit these statistical models for two different objectives: a) the discrimination of different fishing fleets from fishing vessel trajectories and the application to the detection and assessment of illegal fishing activities, b) the assessment of a spatialized fishing effort from VMS data. We report good recognition rate (about 97%) for the former task and our experiments support the potential for an operational exploration of the proposed approach. Due to limited at-sea observers¿ data, only preliminary analyses could be carried out for the proposed VMS-derived fishing effort. Beyond potential methodological developments, this thesis emphasizes the importance of high-quality and representative at-sea observer data for further developing the exploitation of VMS data both for research and operational issues.