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 estado

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

Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Development and Psychometric Properties of the Questionnaire for Assessing Educational Podcasts (QAEP).

The aim of this research was to develop and validate the Questionnaire for Assessing Educational Podcasts (QAEP), an instrument designed to gather students’ views about four dimensions of educational podcasts: access and use, design and structure, content adequacy, and value as an aid to learning. In study 1 we gathered validity evidence based on test content by asking a panel of experts to rate the clarity and relevance of items. Study 2 examined the psychometric properties of the QAEP, including confirmatory factor analysis with cross-validation to test the factor structure of the questionnaire, as well as item and reliability analysis. The results from study 1 showed that the experts considered the items to be clearly worded and relevant in terms of their content. The results from study 2 showed a factor structure consistent with the underlying dimensions, as well as configural and metric invariance across groups. The item analysis and internal consistency for scores on each factor and for total scores were also satisfactory. The scores obtained on the QAEP provide teachers with direct student feedback and highlight those aspects that need to be enhanced in order to improve the teaching/learning process.The research reported in this paper was supported by a Teaching Innovation Project (PIE17-012), funded by the University of Malaga

Análisis de la utilización de espacios o locales del Recinto Universitaro "Rubén Darío"

La Universidad Nacional Autónoma de Nicaragua (UNAN-MANAGUA), es una entidad dedicada a la educación superior, ésta universidad presenta problemas con el control en la utilización de sus instalaciones, siendo esta una de las causas que generan retrasos en el proceso de la planificación de sus actividades, así como un factor básico para la toma de decisiones al momento de sus proyecciones de los diferentes programas. Uno de los factores que afecta el funcionamiento adecuado del recinto es el crecimiento masivo de estudiantes que cada año ingresan; además de la infraestructura, en la cual se encontró que en un 12% de las edificaciones, ya cumplió su vida útil. En este documento se presenta el estado actual del Recinto Universitario “Rubén Darío”, el uso de cada uno de los edificios, esto mediante la ayuda de un inventario de las instalaciones tanto de los edificios pares e impares con que cuenta la UNAN-MANAGUA. Mediante el inventario se dará a conocer la cantidad de Aulas con que dispone la UNAN-MANAGUA, áreas administrativas, sala de medios, laboratorios y otros. Además de conocer datos relacionados a estas instalaciones, como son; el año de construcción, área, y la capacidad con la que cuentan. La disponibilidad de terrenos con que cuenta el Recinto Universitario “Rubén Darío”, será otro aspecto que se considerara, ya que es de vital importancia para el futuro de la UNAN-MANAGUA, para la construcción de nuevas edificaciones. La capacidad con que el Recinto Universitario “Rubén Darío” Cuenta y brinda a los estudiantes. La cual se obtendrá mediante la información obtenida del inventario elaborado y datos obtenidos de la administración de la UNAN-MANAGUA. La cual se hará por turno de clases que esta entidad brinda a los diferentes estudiantes del país. Otro punto a tomar en cuenta es pronosticar el crecimiento de la Matricula del Recinto Universitario “Rubén Darío”, para un periodo de 15 años, tomando en cuenta datos histórico generados por la administración de la UNAN-MANAGUA que darán el punto de partida, para conocer el crecimiento de la población Estudiantil. El rendimiento de la institución es un dato muy importante ya que mediante ella se conocerá la relación que existe entre la cantidad de alumnos que entran a la universidad y la cantidad de profesionales que genera, lo cual representara el rendimiento del Recinto Universitario “Rubén Darío

Characterization of patients with osteoarthritis of the knee. Comprehensive Diagnostic Centre of Concepción

Introduction: Gonarthrosis of the knee is a chronic inflammatory condition presents in patients over 40; obesity as risk factor is of increasing interest for health systems and according to World Health Statistics of the World Health Organization, Venezuela is the country of South America with the highest rates of this condition. Statistics of the National Institute of Nutrition of Venezuela show that Zulia rates are over the national average with 35% of obese. In the practice of Traumatology of La Cañada Municipality we have observed a high incidence of patients with overweight and it stimulated us to do the present study. Objective: to characterize the patients with Gonarthrosis of knee those were attended in the CDC. Material and Methods: a descriptive transversal study was conducted in the CDC of La Cañada de Urdaneta Municipality, Zulia Province, Venezuela from January 2012 to December 2014 with a sample of 360 patients out of a universe of 6740. Results: the lowest age was 35 years and the highest 93, mean 55.5 years. 86.6% were female and half of the patients had family history of osteoarthritis of the knee, while 93% were overweight. Chief complain of 60% of the patients was pain for one year or less. 49 % of the cases showed severe to moderate X ray signs as well as decrease of the muscular force of lower limbs in 71% of the patients. Conclusions: the risk factor most influenced was increased BMI in patients  with Gonarthrosis. Keywords: gonarthrosis of the knee, risk factors, obesity, body mass index.</p

Adarules: Learning rules for real-time road-traffic prediction

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyze the situation in a commercial real-time prediction system with its current problems and limitations. We analyze issues related to the use of spatiotemporal information to reconstruct the traffic state. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic state prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network, which we call as “structure learning”. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. (Part of special issue: 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary)Peer ReviewedPostprint (published version

Enfermedad de Kirner. Presentación de caso

Introducción: la deformidad de Kirner, descrita por J. Kirner en 1927, es un arqueamiento simétrico, palmar, y radial, indoloro y progresivo de las falanges de los quintos dedos de las manos. No se ha reportado una edad precisa en su comienzo. El paciente más joven registrado referido en la literatura es de 5 años de edad y nunca se observó en el recién nacido. Objetivo: presentar la enfermedad de Kirner en un niño de 4 años que desde el nacimiento presentaba la deformidad, y no tenía antecedentes familiares de esta. Presentación del caso: se describe un paciente de 4 años que desde su nacimiento comenzó a presentar cierta deformidad en el 5to dedo de ambas manos, que se agudizó con el pasar de los años. Ahora presenta deformidad por arqueamiento palmar y radial del 5to dedo en ambas manos. No presenta antecedentes familiares. Se diagnostica como Enfermedad de Kirner. Conclusiones: se comprobó el diagnóstico de la enfermedad de Kirner en un niño de 4 años que la venía padeciendo desde su nacimiento y sin antecedentes familiares.Palabras clave: deformidad de Kirner, deformidad del meñique, arqueamiento de la falange terminal.ABSTRACTIntroduction: Kirner’s deformity, was described by J. Kirner, is a palmar, radial, painless and progressive arching of the 5th fingers of hands. There are not precise ages of its onset. The youngest patient reported on the literature is 5 years old and the disease has not been observed in newborn ever. Objective: to show a Kirner´s disease in a male child 4 years old presenting this deformity since birth without parent background. Case presentation: it is described a 4 years old patient that since birthday began to present some deformity in its 5th in both hands becoming acute meanwhile years were passing. He has a deformity by palmar and radial arching of his 5th finger in both hands. He does not show parents background. Kirner´s disease was diagnosed. Conclusions: the Kirner´s disease diagnoses was proved in a child 4 years old which suffering the disease since birthday without parent´s background.Key words: Kirner’s deformity, deformity of the little fingers, curving of the terminal phalanges. 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