Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

Extension of the PWM-based encoding-decoding algorithm for Spiking Neural Networks

[Resumen] Las Redes Neuronales de Impulsos (Spiking Neural Networks, SNN) son la última generación de redes neuronales y tratan de imitar con mayor fidelidad el funcionamiento del cerebro humano codificando la información a través de spikes o series de impulsos. Debido a que la mayoría de procesos reales son analógicos, para emplear este tipo de redes es necesario el uso de algoritmos de codificación y decodificación. El algoritmo de codificación basado en PWM es un novedoso algoritmo temporal de codificación que supera con creces a sus algoritmos predecesores en la precisión a la hora de construir y reconstruir la señal original. A pesar de sus múltiples ventajas, este algoritmo necesita dos puntos cronológicos de la serie temporal original para poder codificar. En este sentido, resulta de interés poder aplicar este tipo de codificación en otro tipo de aplicaciones, como el tratamiento de imágenes, en las que no existe orden cronológico. Por tanto, en este trabajo se presenta una extensión de este algoritmo de codificación para que no sea necesario el uso de dos valores temporales consecutivos y así poder aplicarlo a cualquier tipo de aplicación. Además, la nueva extensión permite reducir en más de un 50% el coste computacional de los procesos de codificación y decodificación.[Abstract] Spiking Neural Networks (SNN) are the latest generation of neural networks and attempt to mimic human brain functioning more closely by encoding the information through spike trains. Since most of the real processes are analog, SNN requires the use of encoding-decoding algorithms. The PWM-based encoding-decoding algorithm is a novel temporal encoding algorithm that surpasses its predecessor algorithms in terms of precision. Despite its many advantages, this algorithm requires two chronological values from the original time series in order to encode a spike. In this sense, it is also interesting to be able to apply this algorithm to other types of application, such as image processing, where it does not exist a chronogical order of the points. Hence, this paper proposes an extension of the PWM-based encoding-decoding algorithm, in which is not necessary to employ two consecutive values in the encoding process, enabling the algorithm to be applied to any type of application. In addition, the new extension reduces the computational cost of encoding and decoding processes by more than 50 %.Gobierno Vasco; PIBA 2020 1 0008Gobierno Vasco; IT1726-22Ministerio de Ciencia e Innovación; PID2020-112667RB-I0

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field