Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods

Probabilistic models for human behavior learning

The problem of human behavior learning is a popular interdisciplinary research topic that has been explored from multiple perspectives, with a principal branch of study in the context of computer vision systems and activity recognition. However, the statistical methods used in these frameworks typically assume short time scales, usually of minutes or even seconds. The emergence of mobile electronic devices, such as smartphones and wearables, has changed this paradigm as long as we are now able to massively collect digital records from users. This collection of smartphone-generated data, whose attributes are obtained in an unobtrusive manner from the devices via multiple sensors and apps, shape the behavioral footprint that is unique for everyone of us. At an individual level, the data projection also di ers from person to person, as not all sensors are equal, neither the apps installed, or the devices used in the real life. This point actually reflects that learning the human behavior from the digital signature of users is an arduous task, that requires to fuse irregular data. For instance, collections of samples that are corrupted, heterogeneous, outliers or have shortterm correlations. The statistical modelling of this sort of objects is one of the principal contributions of this thesis, that we study from the perspective of Gaussian processes (gp). In the particular case of humans, as well as many other life species in our world, we are inherently conditioned to the diurnal and nocturnal cycles that everyday shape our behavior, and hence, our data. We can study these cycles in our behavioral representation to see that there exists a perpetual circadian rhytm in everyone of us. This tempo is the 24h periodic component that shapes the baseline temporal structure of our behavior, not the particular patterns that change for every person. Looking to the trajectories and variabilities that our behavior may take in the data, we can appreciate that there is not a single repetitive behavior. Instead, there are typically several patterns or routines, sampled from our own dictionary, that we choose for every special situation. At the same time, these routines are arbitrary combinations of di erents timescales, correlations, levels of mobility, social interaction, sleep quality or will for working during the same hours on weekdays. Together, the properties of human behavior already indicate to us how we shall proceed to model its structure, not as unique functions, but as a dictionary of latent behavioral profiles. To discover them, we have considered latent variable models. The main application of the statistical methods developed for human behavior learning appears as we look to medicine. Having a personalized model that is accurately fitted to the behavioral patterns of some patient of interest, sudden changes in them could be early indicators of future relapses. From a technical point of view, the traditional question use to be if newer observations conform or not to the expected behavior indicated by the already fitted model. The problem can be analyzed from two perspectives that are interrelated, one more oriented to the characterization of that single object as outlier, typically named as anomaly detection, and another focused in refreshing the learning model if no longer fits to the new sequential data. This last problem, widely known as change-point detection (cpd) is another pillar of this thesis. These methods are oriented to mental health applications, and particularly to the passive detection of crisis events. The final goal is to provide an early detection methodology based on probabilistic modeling for early intervention, e.g. prevent suicide attempts, on psychiatric outpatients with severe a ective disorders of higher prevalence, such as depression or bipolar diseases.El problema de aprendizaje del comportamiento humano es un tema de investigación interdisciplinar que ha sido explorado desde múltiples perspectivas, con una línea de estudio principal en torno a los sistemas de visión por ordenador y el reconocimiento de actividades. Sin embargo, los métodos estadísticos usados en estos casos suelen asumir escalas de tiempo cortas, generalmente de minutos o incluso segundos. La aparición de tecnologías móviles, tales como teléfonos o relojes inteligentes, ha cambiado este paradigma, dado que ahora es posible recolectar ingentes colecciones de datos a partir de los usuarios. Este conjunto de datos generados a partir de nuestro teléfono, cuyos atributos se obtienen de manera no invasiva desde múltiples sensores y apps, conforman la huella de comportamiento que es única para cada uno de nosotros. A nivel individual, la proyección sobre los datos difiere de persona a persona, dado que no todos los sensores son iguales, ni las apps instaladas así como los dispositivos utilizados en la vida real. Esto precisamente refleja que el aprendizaje del comportamiento humano a partir de la huella digital de los usuarios es una ardua tarea, que requiere principalmente fusionar datos irregulares. Por ejemplo, colecciones de muestras corruptas, heterogéneas, con outliers o poseedoras de correlaciones cortas. El modelado estadístico de este tipo de objetos es una de las contribuciones principales de esta tesis, que estudiamos desde la perspectiva de los procesos Gaussianos (gp). En el caso particular de los humanos, así como para muchas otras especies en nuestro planeta, estamos inherentemente condicionados a los ciclos diurnos y nocturnos que cada día dan forma a nuestro comportamiento, y por tanto, a nuestros datos. Podemos estudiar estos ciclos en la representación del comportamiento que obtenemos y ver que realmente existe un ritmo circadiano perpetuo en cada uno de nosotros. Este tempo es en realidad la componente periódica de 24 horas que construye la base sobre la que se asienta nuestro comportamiento, no únicamente los patrones que cambian para cada persona. Mirando a las trayectorias y variabilidades que nuestro comportamiento puede plasmar en los datos, podemos apreciar que no existe un comportamiento único y repetitivo. En su lugar, hay varios patrones o rutinas, obtenidas de nuestro propio diccionario, que elegimos para cada situación especial. Al mismo tiempo, estas rutinas son combinaciones arbitrarias de diferentes escalas de tiempo, correlaciones, niveles de movilidad, interacción social, calidad del sueño o iniciativa para trabajar durante las mismas horas cada día laborable. Juntas, estas propiedades del comportamiento humano nos indican como debemos proceder a modelar su estructura, no como funciones únicas, sino como un diccionario de perfiles ocultos de comportamiento, Para descubrirlos, hemos considerado modelos de variables latentes. La aplicación principal de los modelos estadísticos desarrollados para el aprendizaje de comportamiento humano aparece en cuanto miramos a la medicina. Teniendo un modelo personalizado que está ajustado de una manera precisa a los patrones de comportamiento de un paciente, los cambios espontáneos en ellos pueden ser indicadores de futuras recaídas. Desde un punto de vista técnico, la pregunta clásica suele ser si nuevas observaciones encajan o no con lo indicado por el modelo. Este problema se puede enfocar desde dos perspectivas que están interrelacionadas, una más orientada a la caracterización de aquellos objetos como outliers, que usualmente se conoce como detección de anomalías, y otro enfocado en refrescar el modelo de aprendizaje si este deja de ajustarse debidamente a los nuevos datos secuenciales. Este último problema, ampliamente conocido como detección de puntos de cambio (cpd) es otro de los pilares de esta tesis. Estos métodos se han orientado a aplicaciones de salud mental, y particularmente, a la detección pasiva de eventos críticos. El objetivo final es proveer de una metodología de detección temprana basada en el modelado probabilístico para intervenciones rápidas. Por ejemplo, de cara a prever intentos de suicidio en pacientes fuera de hospitales con trastornos afectivos severos de gran prevalencia, como depresión o síndrome bipolar.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Pablo Martínez Olmos.- Secretario: Daniel Hernández Lobato.- Vocal: Javier González Hernánde

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Probabilistic Models and Natural Language Processing in Health

The treatment of mental disorders nowadays entails a wide variety of still non-solved tasks such as misdiagnosis or delayed diagnosis. During this doctoral thesis we study and develop different models that can serve as potential tools for the clinician labor. Among our proposals, we outline two main lines of research, Natural Language Processing and probabilistic methods. In Chapter 2, we start our thesis with a regularization mechanism used in language models and specially effective in Transformer-based architectures, where we call it NoRBERT, from Noisy Regularized Bidirectional Representations from Transformers [9], [15]. According to the literature, we found out that regularization in NLP is a low explored field limited to the use of general mechanisms such as dropout [57] or early stopping [58]. In this landscape, we propose a novel approach to combine any LM with Variational Auto-Encoders [23]. VAEs belong to deep generative models, with the construction of a regular latent space that permits the reconstruction of the input samples throughout an encoder and decoder networks. Our VAE is based in a prior distribution of a mixture of Gaussians (GMVAE), what gives the model the chance to capture some multimodal information. Combining both, Transformers and GMVAEs we build an architecture capable of imputing missing words from a text corpora in a diverse topic space as well as improve BLEU score in the reconstruction of the data base. Both results depend on the depth of the regularized layer from the Transformer Encoder. The regularization in essence is formed by the GMVAE reconstruction of the Transformer embeddings at some point in the architecture, adding structure noise that helps the model a better generalization. We show improvements in BERT[15], RoBERTa [16] and XLM-R [17] models, verified in different datasets and we also provide explicit examples of sentences reconstructed by Top NoRBERT. In addition, we validate the abilities of our model in data augmentation, improving classification accuracy and F1 score in various datasets and scenarios thanks to augmented samples generated by NoRBERT. We study some variations in the model, Top, Deep and contextual NoRBERT, the latter based in the use of contextual words to reconstruct the embeddings in the corresponding Transformer layer. We continue with the Transformers line of research in Chapter 3, proposing PsyBERT. PsyBERT, as the own name refers, is a BERT-based [15] architecture suitably modified to work in Electronic Health Records from psychiatry patients. It is inspired by BEHRT [19], also devoted to EHRs in general health. We distinguish our model from the training methodology and the embedding layer. In a similar way that with NoRBERT, we find the utility of using a Masked Language Modeling (MLM) policy without no finetuning or specific-task layer at all. On the one hand, we used MLM in NoRBERT to solve the task of imputing missing words, finishing the aim of the model in generating new sentences by inputs with missing information. On the other hand, we firstly propose the use of PsyBERT such as tool to fill the missing diagnoses in the EHR as well as correct misdiagnosed cases. After this task, we also apply PsyBERT in delusional disorder detection. On the contrary, in this scenario we apply a multi-label classification layer, that aims to compute the probability of the different diagnoses in the last visit of the patient to the hospital. From these probabilities, we analyse delusional cases and propose a tool to detect potential candidates of this mental disorder. In both tasks, we make use of several fields obtained from the patient EHR, such as age, sex, diagnoses, treatments of psychiatric history and propose a method capable of combining heterogeneous data to help the diagnosis in mental health. During these works, we point out the problematic in the quality of the data from the EHRs [104], [105] and the great advantage that medical assistance tools like our model can provide. We do not only solve a classification problem with more than 700 different illnesses, but we bring a model to help doctors in the diagnosis of very complex scenarios, with comorbidity, long periods of patient exploration by traditional methodology or low prevalence cases. We present a powerful method treating a problematic with great necessity. Following the health line of research and psychiatry application, we analyse in Chapter 4 a probabilistic method to search for behavioral pattern in patients also with mental disorders. In this case it is not the method the contribution of the work but the application and results in collaboration with the clinician interpretation. The model is called SPFM (Sparse Poisson Factorization Model) [22] and consist on a non-parametric probabilistic model based on the Indian Buffet Process (IBP) [20], [21]. It is a exploratory method capable of decomposing the input data in sparse matrixes. For that, it imposes the Poisson distribution to the product of two matrixes, Z and B, both obtained respectively by the IBP and a Gamma distribution. Hence Z corresponds to a binary matrix representing active latent features in a patient data and B weights the contribution of the data characteristics to the latent features. The data we use in the three works described during the chapter refers to different questions from e-health questionnaries. Then, the data characteristics refer to the answer or punctuation on each question and the latent features from different behavioral patterns in a patient regarding the selection of features active in their questionnaires. For example, patient X can present feature 1 and 2 and patient Y may presence feature 1 and 3, giving as a result two different profiles of behavioral. With these procedure we study three scenarios. In the first problematic, we relate the profiles with the diagnoses, finding common patterns among the patients and connections between diseases. We also analyse the grade of critical state and contrast the clinician judgment via the Clinical Global Impression (CGI). In the second scenario, we pursue a similar study and find out connections between disturbed sleeping patterns and clinical markers of wish to die. We focus this analysis in patients with suicidal thoughts due to the problematic that those individuals suppose as a major public health issue [175]. In this case we vary the questionnarie and the data sample, obtaining different profiles also with important information to interpret by the psychiatrist. The main contribution of this work is the proportion of a mechanism capable of helping with detection and prevention of suicide. Finally, the third work comprehend a behavioral pattern study in mental health patient before and during covid-19 lockdown. We did not want to lose the chance to contribute during coronavirus disease outbreak and presented a study about the changes in psychiatric patients during the alarm state. We analyse again the profiles with the previous e-health questionnaire and discover that the self-reported suicide risk decreased during the lockdown. These results contrast with others studies [237] and suppose signs for an increase in suicidal ideation once the crisis ceases. Finally, Chapter 5 propose a regularization mechanism based in a theoretical idea from [245] to obtain a variance reduction in the real risk. We interpret the robust regularized risk that those authors propose in a two-step mechanism formed by the minimization of the weighted risk and the maximization of a robust objective and suggest an idea to apply this methodology in a way to select the samples from the mini-batch in a deep learning set up. We study different variations of repeating the worst performed samples from the previous mini-bath during the training procedure and show proves of improvements in the accuracy and faster convergence rates of a image classification problem with different architectures and datasets.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Joaquín Míguez Arenas.- Secretario: Francisco Jesús Rodríguez Ruiz.- Vocal: Santiago Ovejero Garcí

Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Predicting pedestrian crossing intentions using contextual information

El entorno urbano es uno de los escenarios m as complejos para un veh culo aut onomo, ya que lo comparte con otros tipos de usuarios conocidos como usuarios vulnerables de la carretera, con los peatones como mayor representante. Estos usuarios se caracterizan por su gran dinamicidad. A pesar del gran n umero de interacciones entre veh culos y peatones, la seguridad de estos ultimos no ha aumentado al mismo ritmo que la de los ocupantes de los veh culos. Por esta raz on, es necesario abordar este problema. Una posible estrategia estar a basada en conseguir que los veh culos anticipen el comportamiento de los peatones para minimizar situaciones de riesgo, especialmente presentes en el momento de cruce. El objetivo de esta tesis doctoral es alcanzar dicha anticipaci on mediante el desarrollo de t ecnicas de predicci on de la acci on de cruce de peatones basadas en aprendizaje profundo. Previo al dise~no e implementaci on de los sistemas de predicci on, se ha desarrollado un sistema de clasi caci on con el objetivo de discernir a los peatones involucrados en la escena vial. El sistema, basado en redes neuronales convolucionales, ha sido entrenado y validado con un conjunto de datos personalizado. Dicho conjunto se ha construido a partir de varios conjuntos existentes y aumentado mediante la inclusi on de im agenes obtenidas de internet. Este paso previo a la anticipaci on permitir a reducir el procesamiento innecesario dentro del sistema de percepci on del veh culo. Tras este paso, se han desarrollado dos sistemas como propuesta para abordar el problema de predicci on. El primer sistema, basado en redes convolucionales y recurrentes, obtiene una predicci on a corto plazo de la acci on de cruce realizada un segundo en el futuro. La informaci on de entrada al modelo est a basada principalmente en imagen, que permite aportar contexto adicional del peat on. Adem as, el uso de otras variables relacionadas con el peat on junto con mejoras en la arquitectura, permiten mejorar considerablemente los resultados en el conjunto de datos JAAD. El segundo sistema se basa en una arquitectura end-to-end basado en la combinaci on de redes neuronales convolucionales tridimensionales y/o el codi cador de la arquitectura Transformer. En este modelo, a diferencia del anterior, la mayor a de las mejoras est an centradas en transformaciones de los datos de entrada. Tras analizar dichas mejoras, una serie de modelos se han evaluado y comparado con otros m etodos utilizando tanto el conjunto de datos JAAD como PIE. Los resultados obtenidos han conseguido liderar el estado del arte, validando la arquitectura propuesta.The urban environment is one of the most complex scenarios for an autonomous vehicle, as it is shared with other types of users known as vulnerable road users, with pedestrians as their principal representative. These users are characterized by their great dynamicity. Despite a large number of interactions between vehicles and pedestrians, the safety of pedestrians has not increased at the same rate as that of vehicle occupants. For this reason, it is necessary to address this problem. One possible strategy would be anticipating pedestrian behavior to minimize risky situations, especially during the crossing. The objective of this doctoral thesis is to achieve such anticipation through the development of crosswalk action prediction techniques based on deep learning. Before the design and implementation of the prediction systems, a classi cation system has been developed to discern the pedestrians involved in the road scene. The system, based on convolutional neural networks, has been trained and validated with a customized dataset. This set has been built from several existing sets and augmented by including images obtained from the Internet. This pre-anticipation step would reduce unnecessary processing within the vehicle perception system. After this step, two systems have been developed as a proposal to solve the prediction problem. The rst system is composed of convolutional and recurrent encoder networks. It obtains a short-term prediction of the crossing action performed one second in the future. The input information to the model is mainly image-based, which provides additional pedestrian context. In addition, the use of pedestrian-related variables and architectural improvements allows better results on the JAAD dataset. The second system is an end-to-end architecture based on the combination of threedimensional convolutional neural networks and/or the Transformer architecture encoder. In this model, most of the proposed and investigated improvements are focused on transformations of the input data. After an extensive set of individual tests, several models have been trained, evaluated, and compared with other methods using both JAAD and PIE datasets. Obtained results are among the best state-of-the-art models, validating the proposed architecture

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

An adaptive, fault-tolerant system for road network traffic prediction using machine learning

This thesis has addressed the design and development of an integrated system for real-time traffic forecasting based on machine learning methods. Although traffic prediction has been the driving motivation for the thesis development, a great part of the proposed ideas and scientific contributions in this thesis are generic enough to be applied in any other problem where, ideally, their definition is that of the flow of information in a graph-like structure. Such application is of special interest in environments susceptible to changes in the underlying data generation process. Moreover, the modular architecture of the proposed solution facilitates the adoption of small changes to the components that allow it to be adapted to a broader range of problems. On the other hand, certain specific parts of this thesis are strongly tied to the traffic flow theory. The focus in this thesis is on a macroscopic perspective of the traffic flow where the individual road traffic flows are correlated to the underlying traffic demand. These short-term forecasts include the road network characterization in terms of the corresponding traffic measurements –traffic flow, density and/or speed–, the traffic state –whether a road is congested or not, and its severity–, and anomalous road conditions –incidents or other non-recurrent events–. The main traffic data used in this thesis is data coming from detectors installed along the road networks. Nevertheless, other kinds of traffic data sources could be equally suitable with the appropriate preprocessing. This thesis has been developed in the context of Aimsun Live –a simulation-based traffic solution for real-time traffic prediction developed by Aimsun–. The methods proposed here is planned to be linked to it in a mutually beneficial relationship where they cooperate and assist each other. An example is when an incident or non-recurrent event is detected with the proposed methods in this thesis, then the simulation-based forecasting module can simulate different strategies to measure their impact. Part of this thesis has been also developed in the context of the EU research project "SETA" (H2020-ICT-2015). The main motivation that has guided the development of this thesis is enhancing those weak points and limitations previously identified in Aimsun Live, and whose research found in literature has not been especially extensive. These include: • Autonomy, both in the preparation and real-time stages. • Adaptation, to gradual or abrupt changes in traffic demand or supply. • Informativeness, about anomalous road conditions. • Forecasting accuracy improved with respect to previous methodology at Aimsun and a typical forecasting baseline. • Robustness, to deal with faulty or missing data in real-time. • Interpretability, adopting modelling choices towards a more transparent reasoning and understanding of the underlying data-driven decisions. • Scalable, using a modular architecture with emphasis on a parallelizable exploitation of large amounts of data. The result of this thesis is an integrated system –Adarules– for real-time forecasting which is able to make the best of the available historical data, while at the same time it also leverages the theoretical unbounded size of data in a continuously streaming scenario. This is achieved through the online learning and change detection features along with the automatic finding and maintenance of patterns in the network graph. In addition to the Adarules system, another result is a probabilistic model that characterizes a set of interpretable latent variables related to the traffic state based on the traffic data provided by the sensors along with optional prior knowledge provided by the traffic expert following a Bayesian approach. On top of this traffic state model, it is built the probabilistic spatiotemporal model that learns the dynamics of the transition of traffic states in the network, and whose objectives include the automatic incident detection.Esta tesis ha abordado el diseño y desarrollo de un sistema integrado para la predicción de tráfico en tiempo real basándose en métodos de aprendizaje automático. Aunque la predicción de tráfico ha sido la motivación que ha guiado el desarrollo de la tesis, gran parte de las ideas y aportaciones científicas propuestas en esta tesis son lo suficientemente genéricas como para ser aplicadas en cualquier otro problema en el que, idealmente, su definición sea la del flujo de información en una estructura de grafo. Esta aplicación es de especial interés en entornos susceptibles a cambios en el proceso de generación de datos. Además, la arquitectura modular facilita la adaptación a una gama más amplia de problemas. Por otra parte, ciertas partes específicas de esta tesis están fuertemente ligadas a la teoría del flujo de tráfico. El enfoque de esta tesis se centra en una perspectiva macroscópica del flujo de tráfico en la que los flujos individuales están ligados a la demanda de tráfico subyacente. Las predicciones a corto plazo incluyen la caracterización de las carreteras en base a las medidas de tráfico -flujo, densidad y/o velocidad-, el estado del tráfico -si la carretera está congestionada o no, y su severidad-, y la detección de condiciones anómalas -incidentes u otros eventos no recurrentes-. Los datos utilizados en esta tesis proceden de detectores instalados a lo largo de las redes de carreteras. No obstante, otros tipos de fuentes de datos podrían ser igualmente empleados con el preprocesamiento apropiado. Esta tesis ha sido desarrollada en el contexto de Aimsun Live -software desarrollado por Aimsun, basado en simulación para la predicción en tiempo real de tráfico-. Los métodos aquí propuestos cooperarán con este. Un ejemplo es cuando se detecta un incidente o un evento no recurrente, entonces pueden simularse diferentes estrategias para medir su impacto. Parte de esta tesis también ha sido desarrollada en el marco del proyecto de la UE "SETA" (H2020-ICT-2015). La principal motivación que ha guiado el desarrollo de esta tesis es mejorar aquellas limitaciones previamente identificadas en Aimsun Live, y cuya investigación encontrada en la literatura no ha sido muy extensa. Estos incluyen: -Autonomía, tanto en la etapa de preparación como en la de tiempo real. -Adaptación, a los cambios graduales o abruptos de la demanda u oferta de tráfico. -Sistema informativo, sobre las condiciones anómalas de la carretera. -Mejora en la precisión de las predicciones con respecto a la metodología anterior de Aimsun y a un método típico usado como referencia. -Robustez, para hacer frente a datos defectuosos o faltantes en tiempo real. -Interpretabilidad, adoptando criterios de modelización hacia un razonamiento más transparente para un humano. -Escalable, utilizando una arquitectura modular con énfasis en una explotación paralela de grandes cantidades de datos. El resultado de esta tesis es un sistema integrado –Adarules- para la predicción en tiempo real que sabe maximizar el provecho de los datos históricos disponibles, mientras que al mismo tiempo también sabe aprovechar el tamaño teórico ilimitado de los datos en un escenario de streaming. Esto se logra a través del aprendizaje en línea y la capacidad de detección de cambios junto con la búsqueda automática y el mantenimiento de los patrones en la estructura de grafo de la red. Además del sistema Adarules, otro resultado de la tesis es un modelo probabilístico que caracteriza un conjunto de variables latentes interpretables relacionadas con el estado del tráfico basado en los datos de sensores junto con el conocimiento previo –opcional- proporcionado por el experto en tráfico utilizando un planteamiento Bayesiano. Sobre este modelo de estados de tráfico se construye el modelo espacio-temporal probabilístico que aprende la dinámica de la transición de estadosPostprint (published version