Plataforma de informação de tráfego para redução de consumos e emissões

Doutoramento em Engenharia MecânicaApesar das recentes inovações tecnológicas, o setor dos transportes continua a exercer impactes significativos sobre a economia e o ambiente. Com efeito, o sucesso na redução das emissões neste setor tem sido inferior ao desejável. Isto deve-se a diferentes fatores como a dispersão urbana e a existência de diversos obstáculos à penetração no mercado de tecnologias mais limpas. Consequentemente, a estratégia “Europa 2020” evidencia a necessidade de melhorar a eficiência no uso das atuais infraestruturas rodoviárias. Neste contexto, surge como principal objetivo deste trabalho, a melhoria da compreensão de como uma escolha de rota adequada pode contribuir para a redução de emissões sob diferentes circunstâncias espaciais e temporais. Simultaneamente, pretende-se avaliar diferentes estratégias de gestão de tráfego, nomeadamente o seu potencial ao nível do desempenho e da eficiência energética e ambiental. A integração de métodos empíricos e analíticos para avaliação do impacto de diferentes estratégias de otimização de tráfego nas emissões de CO2 e de poluentes locais constitui uma das principais contribuições deste trabalho. Esta tese divide-se em duas componentes principais. A primeira, predominantemente empírica, baseou-se na utilização de veículos equipados com um dispositivo GPS data logger para recolha de dados de dinâmica de circulação necessários ao cálculo de emissões. Foram percorridos aproximadamente 13200 km em várias rotas com escalas e características distintas: área urbana (Aveiro), área metropolitana (Hampton Roads, VA) e um corredor interurbano (Porto-Aveiro). A segunda parte, predominantemente analítica, baseou-se na aplicação de uma plataforma integrada de simulação de tráfego e emissões. Com base nesta plataforma, foram desenvolvidas funções de desempenho associadas a vários segmentos das redes estudadas, que por sua vez foram aplicadas em modelos de alocação de tráfego. Os resultados de ambas as perspetivas demonstraram que o consumo de combustível e emissões podem ser significativamente minimizados através de escolhas apropriadas de rota e sistemas avançados de gestão de tráfego. Empiricamente demonstrou-se que a seleção de uma rota adequada pode contribuir para uma redução significativa de emissões. Foram identificadas reduções potenciais de emissões de CO2 até 25% e de poluentes locais até 60%. Através da aplicação de modelos de tráfego demonstrou-se que é possível reduzir significativamente os custos ambientais relacionados com o tráfego (até 30%), através da alteração da distribuição dos fluxos ao longo de um corredor com quatro rotas alternativas. Contudo, apesar dos resultados positivos relativamente ao potencial para a redução de emissões com base em seleções de rotas adequadas, foram identificadas algumas situações de compromisso e/ou condicionantes que devem ser consideradas em futuros sistemas de eco navegação. Entre essas condicionantes importa salientar que: i) a minimização de diferentes poluentes pode implicar diferentes estratégias de navegação, ii) a minimização da emissão de poluentes, frequentemente envolve a escolha de rotas urbanas (em áreas densamente povoadas), iii) para níveis mais elevados de penetração de dispositivos de eco-navegação, os impactos ambientais em todo o sistema podem ser maiores do que se os condutores fossem orientados por dispositivos tradicionais focados na minimização do tempo de viagem. Com este trabalho demonstrou-se que as estratégias de gestão de tráfego com o intuito da minimização das emissões de CO2 são compatíveis com a minimização do tempo de viagem. Por outro lado, a minimização de poluentes locais pode levar a um aumento considerável do tempo de viagem. No entanto, dada a tendência de redução nos fatores de emissão dos poluentes locais, é expectável que estes objetivos contraditórios tendam a ser minimizados a médio prazo. Afigura-se um elevado potencial de aplicação da metodologia desenvolvida, seja através da utilização de dispositivos móveis, sistemas de comunicação entre infraestruturas e veículos e outros sistemas avançados de gestão de tráfego.Despite recent technological innovations, transportation sector is still producing significant impacts on the economy and environment. In fact, the success in reducing transportation emissions has been lower than desirable due to several factors such as the urban sprawl and several barriers to the market penetration of cleaner technologies. Therefore, the “Europe 2020” strategy has emphasised the relevance of improving the efficiency in the transportation networks through the better use of the existing infrastructures. In this context, the main objective of this thesis is increasing the understanding of how proper route choices can contribute to reduce emissions output over different spatial and temporal contexts. Simultaneously, it is intended to evaluate the potential of different traffic management strategies in terms of traffic performance and energy/environmental efficiency. The integration of empirical and analytical methods to assess the impact of different traffic optimization strategies on CO2 emissions and local pollutants constitutes one the main contributions of this work. This thesis has been divided in two main parts. The first is predominantly empirical, using field data as the main source of information. Using GPS equipped vehicles, empirical data for approximately 13200 km of road coverage have been collected to estimate energy and emissions impacts of route choice in three different scenarios: a medium-sized urban area (Aveiro), a metropolitan area (Hampton Roads, VA) and an intercity corridor (Oporto-suburban area). The second part, predominantly analytical, is essentially based on the output of traffic simulators and optimization models. The analytical component was based on the capability of microscopic traffic models to generate detailed emissions information and to generate link-based performance functions. Then, different traffic management strategies were tested to evaluate road networks in terms of traffic performance and emissions. Both outcomes of the empirical and analytical approaches have demonstrated that fuel use and emissions impacts can also be significantly reduced through appropriate route choices and advanced traffic management systems. The empirical assessment of route choice impacts has shown that both during off peak and peak periods, the selection of an appropriate route can lead to significant emissions reduction. Depending on the location, potential emissions savings of CO2 up to 25% and local pollutants up to 60% were found. The analytical approach has demonstrated that it is possible to significantly reduce system environmental costs (30%) by modifying traffic flow distribution along a corridor with 4 alternative routes. However, despite the positive results in terms of the potential for emissions reduction based on appropriate route choices, a number of important trade-offs that need to be considered in future implementations of eco-routing systems. Among these trade-offs it is worth noting that: i) different pollutants may lead to different ecorouting strategies, ii) the minimization of pollutants emissions often involves choosing urban routes (densely populated), iii) for higher penetration levels of eco-routing devices considering local pollutants, system environmental impacts can be higher than if drivers were guided under the traditional devices focused on travel time. With this research, it has been demonstrated that road traffic management strategies focused on minimizing CO2 emissions and fuel consumption can be compatible with the minimization of system travel time. On the other hand the minimization of local pollutants may lead to considerable increases in travel time. However, given the trend rate of reduction in the emissions factors of local pollutants, it is expected that such trade-offs would tend to be minimized in medium term. Thus, the developed methodology has great potential for further real life application, either through the use of nomadic devices, infrastructures to vehicle communication or different advanced traffic management systems

Zero-Emission Heavy-Duty Vehicle Integration in Support of a 100% Renewable Electric Grid

For California and other parts of the world to move towards a net-zero-emission grid, potentially a 100% renewable grid, complementary technologies to support renewable solar and wind integration need to be clearly established. Specifically, the integration of variable and intermittent solar and wind renewable generation requires resources that can respond dynamically to changes in the net load in order to ensure stable grid performance. Zero-emission vehicles (ZEVs), encompassing battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs), are uniquely positioned to (1) support variable renewable generation and provide benefits to the grid while, at the same time (2) reducing emissions from the transportation sector. Due to their disproportionately large contribution to air pollution and greenhouse gas (GHG) emissions, targeting heavy-duty vehicles (HDVs) is essential if reduction goals are to be met. This work assesses the feasibility of heavy duty ZEVs (HD-ZEVs), selecting California as the example. From a technical standpoint, more than half of Class 3-7 vehicle miles travelled (VMT) can be met with heavy-duty BEV product in development today without trip modification. Class 8 trucks have a much lower BEV feasibility due to their longer trip distances and heavy-duty FCEV product becomes more likely. The challenge becomes providing carbon-free fuel, namely renewable electricity for HD-BEVs, and renewable hydrogen for HD-FCEVs. This study assesses the fuel supply chain impact of HD-ZEV deployment on GHG emissions and air quality for the year 2050. HD-BEVs relying on uncoordinated charging can increase peak load demand and hinder the target of achieving zero GHG emissions from the electric grid. Intelligent charging of HD- BEVs and renewable hydrogen production for HD-FCEVs are both effective methods for utilizing otherwise curtailed renewable generation for the support of a zero or near-zero emissions electric grid. This study also finds that moving towards an 80% reduction in GHG emissions from HDVs through ZEV adoption has the co-benefit of significantly reducing ozone and PM2.5 concentrations in key regions of California. In comparison, reducing grid emissions from an 80% reduction to a 100% clean electric grid has a significantly smaller, but not trivial, impact in criteria air pollutant concentrations

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management

Energy Development for Sustainability

Recently, energy development has received significant attention through the promising results of technology development, experimentation, computational modeling, and validation. However, it remains a persistent challenge to produce the needed energy while significantly reducing the environmental effects, such as the emission of greenhouse gases, which lead to climate change. Moreover, technological and economic limitations may also hinder energy development for sustainability. This book entitled Energy Development for Sustainability covers technologies, products, equipment, and devices as well as energy services based on software and data protected by patents and/or trademarks. This book will serve as a collection of the latest scientific and technological approaches to various energy development initiatives for sustainability encompassing novel sonocatalytic application and integrated algal and sludge-based wastewater treatment system, energy storage, sustainable building, gas absorption, organosolv pretreatment, energy usage and CO2 emission in transportation, coal regulation for energy, solar photovoltaic system, torrefaction for fuel production, energy management system, clean energy incubator, biofuels from microalgae, and the influence of COVID-19 on climate change. Overall, this book addresses researchers, advanced students, technical consultants, as well as decision-makers in industries and politics. This book contains comprehensive overview and in-depth technical research papers addressing recent progress in the area of energy development for sustainability. We hope the readers will enjoy this book