Dissertations / Theses on the topic 'Advanced traffic management systems'

Present day air traffc control is reaching its operational limits and accommodating future traffic growth will be a challenging task for air traffic service providers and airline operators. Free Flight is a proposed transition from a highly-structured and centrally-controlled air traffic system to a self-optimized and highly-distributed system. In Free Flight, pilots will have the flexibility of real-time trajectory planning and dynamic route optimization given airspace constraints (traffic, weather etc.). A variety of advanced air traffc management (ATM) concepts are proposed as enabling technologies for the realization of Free Flight. Since these concepts can be exposed to unforeseen and challenging scenarios in Free Flight, they need to be validated and evaluated in order to implement the most effective systems in the field. Evaluation of advanced ATM concepts is a challenging task due to the limitations in the existing scenario generation methodologies and limited availability of a common platform (air traffic simulator) where diverse ATM concepts can be modeled and evaluated. Their rigorous evaluation on safety metrics, in a variety of complex scenarios, can provide an insight into their performance, which can help improve upon them while developing new ones. In this thesis, I propose a non-propriety, non-commercial air traffic simulation system, with a novel representation of airspace, which can prototype advanced ATM concepts such as conflict detection and resolution, airborne weather avoidance and cockpit display of traffic information. I then propose a novel evolutionary computation methodology to algorithmically generate a massive number of conflict scenarios of increasing complexity in order to evaluate conflict detection algorithms. I illustrate the methodology in detail by quantitative evaluation of three conflict detection algorithms, from the literature, on safety metrics. I then propose the use of data mining techniques for the discovery of interesting relationships, that may exist implicitly, in the algorithm's performance data. The data mining techniques formulate the conflict characteristics, which may lead to algorithm failure, using if-then rules. Using the rule sets for each algorithm, I propose an ensemble of conflict detection algorithms which uses a switch mechanism to direct the subsequent conflict probes to an algorithm which is less vulnerable to failure in a given conflict scenario. The objective is to form a predictive model for algorithm's vulnerability which can then be included in an ensemble that can minimize the overall vulnerability of the system. In summary, the contributions of this thesis are: 1. A non-propriety, non-commercial air traffic simulation system with a novel representation of airspace for efficient modeling of advanced ATM concepts. 2. An Ant-based dynamic weather avoidance algorithm for traffic-constrained enroute airspace. 3. A novel representation of 4D air traffic scenario that allows the use of an evolutionary computation methodology to evolve complex conflict scenarios for the evaluation of conflict detection algorithms. 4. An evaluation framework where scenario generation, scenario evaluation and scenario evolution processes can be carried out in an integrated manner for rigorous evaluation of advanced ATM concepts. 5. A methodology for forming an intelligent ensemble of conflict detection algorithms by data mining the scenario space.

Variable speed limit (VSL) systems are important active traffic management tools that are being deployed across the U.S. and indeed around the world for relieving congestion and improving safety. Oregon’s first variable advisory speed limit signs were activated along Oregon Highway 217 in the summer of 2014. The variable advisory speed system is responsive to both congestion and weather conditions. This seven-mile corridor stretches around Western Portland and has suffered from high crash rates and peak period congestion in the past. VSL systems are often deployed to address safety, mobility and sustainability related performance. This research seeks to determine whether the newly implemented variable advisory speed limit system has had measurable impacts on traffic safety and what the scale of the impact has been. The research utilizes a before-after crash analysis with three years of data prior to implementation and around 16 months after. Statistical analysis using an Empirical Bayes (EB) approach will aim to separate the direct impacts of the variable advisory speed limit signs from the long term trends on the highway. In addition, the analysis corrects for the changes in traffic volumes over the study period. Three data sources will be utilized including Washington County 911 call data, Oregon incident reports, and official Oregon Department of Transportation crash data reports. The analysis results are compared between data sources to determine the reliability of 911 call data as a proxy for crash statistics. The conclusions should be able to provide an indication of whether variable advisory speed limits can provide increased safety along high crash corridors.

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users.<br>ID: 031988164; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references.<br>Ph.D.<br>Doctorate<br>Civil, Environmental, and Construction Engineering<br>Engineering and Computer Science<br>Civil Engineering

<p>More and more travellers use navigation-aid software to find the way while driving. Most of todays systems use static maps with little or no information at all about currently yeilding roads conditions and disturbances in the network. It is desirable for such services</p><p>in the future to include information about road works, accidents, surface conditions and other types of events that affects what route is currently the best.</p><p>It is also desirable to notify users about changes in the prerequisites of the chosen route after they have started their trip.</p><p>This thesis investigates methods to include dynamic traffic information in route calculations and notifying users when the characteristics change for their chosen route.</p><p>The thesis utilizes dynamic traffic information from The Swedish Road Agencys (Vägverket) central database for traffic information, TRISS and calculates affected clients with help of positioning through the GSM network.</p>

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 209-219).<br>The Air Traffic Management (ATM) system enables air transportation by ensuring a safe and orderly air traffic flow. As the air transport demand has grown, ATM has become increasingly challenging, resulting in high levels of congestion, flight delays and environmental impacts. To sustain the industry growth foreseen and enable more efficient air travel, it is important to develop mechanisms for better understanding and predicting the air traffic flow behavior and performance in order to assist human decision-makers to deliver improved airspace design and traffic management solutions. This thesis presents a data-driven approach to modeling air traffic flows and analyzes its contribution to supporting system level ATM decision-making. A data analytics framework is proposed for high-fidelity characterization of air traffic flows from large-scale flight tracking data. The framework incorporates a multi-layer clustering analysis to extract spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a detailed characterization of terminal area traffic flows in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the metroplexes analyzed and highlight varied action areas to improve air traffic operations at these systems. Finally, the knowledge derived from flight trajectory data analytics is leveraged to develop predictive and prescriptive models for metroplex configuration and capacity planning decision support. Supervised learning methods are used to create prediction models capable of translating weather forecasts into probabilistic forecasts of the metroplex traffic flow structure and airport capacity for strategic time horizons. To process these capacity forecasts and assist the design of traffic flow management strategies, a new optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal Airport Acceptance Rates in Ground Delay Programs, an overall delay reduction of up to 9.7% is achieved.<br>by Mayara Condé Rocha Murça.<br>Ph. D.

This PhD thesis considers some of the more emerging problems in network modelling, namely the design of survivable hierarchical networks, <i>Traffic Engineering </i>(TE)<i> </i>and generally traffic management in survivable multi-service networks with <i>Quality of Service </i>(QoS) prerequisites and the planning of wireless access networks. So, in the context of the research work presented in this thesis:- Novel survivable hierarchical network design, wireless access network planning and traffic management techniques were developed. These techniques involve optimisation methods based on <i>Linear Programming </i>(LP) and <i>Integer Linear Programming </i>(ILP), as well as heuristic methods based on <i>graph theory  </i>and <i>computational intelligence (genetic optimisation </i>and <i>simulated annealing). </i> A unified framework for off-line TE, on-line/dynamic routing and path restoration (facility restoration) that can be used in survivable multi-service QoS networks was also developed. Existing traffic management techniques were improved so that to support advanced QoS and survivability characteristics. At first, the objectives of this project are presented followed by a brief analysis of the problems encountered in the network design process. Next, the new methods for designing survivable hierarchical networks are analytically described followed by the developed wireless access network design techniques. After that, the novel traffic management methods and the aforementioned framework, developed in the context of this thesis are presented. Test results are provided together with most of the developed methods. The test results actually indicate that the developed methods can efficiently solve small, medium or even large problems, all developed methods are computationally tractable and the performance of the developed heuristic method is very close to this of the corresponding LP and ILP optimisation methods. The new heuristic methods are solved in a fraction of the time (less than 30%) that the equivalent optimisation methods are solved. Note that the specially developed design and simulation software tool <i>NetLab </i>was used in order to test and evaluate the new design and traffic management methods. Finally, a summary of the work carried out and the results achieved is presented followed by the conclusions and suggestions for further work.

State-of-the-art diesel exhaust gas aftertreatment systems have proved to substantially decrease vehicles’ emissions. However, their effectiveness depends on the temperature of the exhaust gas and catalysts to activate the emissions’ conversion reactions. In this research study, different strategies for thermal management of diesel aftertreatment systems were investigated to reduce vehicles’ emissions. A thermal energy storage (TES) system was developed and implemented for a light-duty diesel aftertreatment system. In this approach, the extra thermal energy of the exhaust gas during engine’s high-load conditions can be stored and reused when required, in order to maintain the emissions’ conversion reactions during a driving cycle. The results indicated that by increasing the thermal conductivity of the thermal energy storage medium and the catalyst’s substrate, the TES system can reduce the vehicle’s cumulative CO and THC emissions by 91.7% and 41.2% respectively. Active heating of the aftertreatment system was studied to provide the catalysts with the required thermal energy, in order to shorten the catalyst’s light-off period and also prevent the catalyst from light-out during a driving cycle. A pulsating electrical catalyst heating strategy and a combined electrical heating and fuel post-injection approach were developed to increase the heating efficiency while minimizing the vehicle’s emissions.

Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of traffic state estimation techniques, which produce estimate of the traffic states with less uncertainty than the prediction or measurement alone. On the other hand, research studies highlighting estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. This research introduces a framework which integrates real-time traffic state estimate with applications of DTA to optimize network performance during uncertain traffic conditions through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended Kalman Filter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on available routes in a traffic network and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments, the proposed estimation and information framework is applied to optimize network performance during traffic incident on a two route network. The proposed framework significantly improved the network performance and commuters’ travel time when compared with no-information scenario during the incident. The application of the formulated methodology is extended to model day-to-day dynamics of traffic flow and route choice with time-varying traffic demand. The day-to-day network performance is improved by providing accurate and reliable traveller information. The implementation of the proposed framework through numerical experiments shows a significant improvement in daily travel times and stability in day-to-day performance of the network when compared with no-information scenario. The use of model based real-time traffic state estimation in DTA models allows modelling and estimating behaviour parameters in DTA models which improves the accuracy of the modelling process. In this research, a framework is proposed to model commuters’ level of trust in the information provided which defines the weight given to the information by commuters while they update their perception about expected travel time. A methodology is formulated to model and estimate logit parameter for perception variation among commuters for expected travel time based on measurements from traffic sensors and estimated traffic state. The application of the proposed framework to a test network shows that the model accurately estimated the value of logit parameter when started with a different initial value of the parameter.

With the development of advanced driving assistance systems, in-vehicle communication and information systems, there are situations where the driver becomes overloaded by information, creating potentially dangerous conditions. In this Thesis a novel strategy is proposed, to prioritise and present information. Firstly two main criteria are extracted, that allow the ability to rank messages: the risk associated with the non-presentation of the message, and its relevance to the environment. Fuzzy cognitive maps enable to represent expert knowledge and model these relationships. Secondly, a strategy to present information is proposed. Using an importance index, calculated from the previous risk and relevance indices, but also information nature, time constraints and access frequency, a set of best interfaces is selected. Furthermore design a model of driver workload is designed, based on the multiple resources theory. By estimating in real time the workload of the driver, the system enables to choose an optimal interface, that should prevent overload. This Thesis presents then the tools developed for the implementation and testing of the model. A video capture and data transfer program, based on the IEEE-1394 bus, enable in-vehicle real-time data capture and collection. Moreover, a software package for replay of the acquired data, analysis and simulation is developed. Finally, the implementation of the prioritisation and presentation strategy is outlined. The last part of this work is dedicated to the experiments and results. Using an experimental vehicle, data in different driving conditions are collected. the experiment is completed by creating data to simulate potentially dangerous situations, where driver is overloaded with information. The results show that the information management and presentation system is able to prevent overload in most conditions. Its structure and design allow to incorporate expert knowledge to refine the classification.

Abstract: This thesis addresses the problem of graceful degradation for air traffic management systems (ATMS). The graceful degradation is the process by which the safety of the airspace is ensured in the event of failures or operational degradation in the system. After listing the main areas where failures and degradation can affect the ATMS, an ontology of the ATMS is proposed. The ontology allows to introduce failures at different levels, track their propagation throughout the system, and measure their operational impact. Then, two operational degradations are studied: The first degradation studied is a reduction in the landing capacity at San Francisco International Airport. The aircraft queueing process for terminal area is modeled and optimized to ensure a graceful degradation. The second degradation encompasses Communication, Navigation and Surveillance systems failures. The graceful degradation is ensured by increasing the spacing distance between aircraft, using novel algorithms of avoidance under uncertainties. Those algorithm also serve as probes to compare the degradation capabilities of different traffic configurations such as Miles-In-Trail and Free-Flight arrivals. Finally, this thesis focuses on monitoring the airspace for potential degradation. The ability and the difficulty of en-route traffic configuration are evaluated using degradation maps. Those maps can be used controller to rapidly and efficiently steer traffic from nominal mode of operations to mode of operations under abnormal conditions. Finally, a monitoring tool for terminal area is presented: the conformance of current flight to pre-identified typical operations is determined in real time. As the number of non-conforming aircraft increases, the complexity seen by air traffic controllers increases, and can become a threat for the airspace safety.

Increasing travel demand sees projections of 11.5 million flights in Europe annually by 2016. The current system is not capable of managing these increases, introducing the need for a redevelopment initiative, the Single Sky, to reduce environmental impact, improve efficiency and manage the increased capacity with even better safety. This is a challenging goal as these systems are characteristically complex arising from numerous interacting objects whose behaviour is affected by history or feedback in an open system resulting in emergent, ordered and disordered behaviour through the structure. It is difficult to understand the system behaviour, which is key to modifying, developing and implementing the desired configurations effectively. Ascertaining the behaviour of such systems requires an understanding of the coupling and interdependencies on a system wide scale, which is not available in the traditional approaches to ATM system analysis. This research addresses the need by developing a simple, inexpensive, non data intensive, qualitative analysis for identifying these relationships and interdependencies through a technique of invoking a change in the system architecture and measuring the deviation experienced by the other elements, with the deviation referred to as impact. An agent based modelling approach was identified as the most suitable and the method was therefore implemented in an agent based environment. The model was verified in a system of 21 airports from the UK and Ireland and a user defined number of flights with at least a departure or arrival airport within the system. Critical review and validation of the model is performed using a combination of scenarios comprising real world events. System analysis and changes in protocol were used to identify phenomenon such as impact propagation and absorption. The combination of impact analysis, approach and the model is shown to provide a simple means for ascertaining the relationships and interdependencies as impact propagates through the system due to aircraft moving through the operational hierarchy. The research identifies the applicability of the technique toward the analysis of other complex systems including ecosystems and financial markets.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.<br>Includes bibliographical references (p. 104-106).<br>by Bruno Miguel Fernández Ruiz.<br>S.M.

This research seeks to investigate how application of Human Factors techniques could be used to improve performance resulting from the use of technical traffic management and SCOOT validation systems. The systems used in both domains have historically been developed without consideration given to the social factors important to their use, designs instead being based solely on technical constraints. In the first stages of the project traffic management is investigated through conduction of a literature review covering the objectives, functions and constraints acting upon Traffic Management Centres (TMCs) in road, rail, maritime and air domains. Congestion management is then considered in urban road TMCs through application of the Event Analysis of Systematic Teamwork (EAST) method based on observational data collected from four TMCs, Bristol, Cardiff, Dorset and Nottingham, in which the tasks, social agents, information and relationships between these elements are considered. The EAST method is then expanded to enable investigation into TMCs’ resilience, providing further knowledge about the domain. The later stages of the project are concerned with SCOOT validation, the process by which adaptively controlled traffic lights using SCOOT are set up to reflect real traffic conditions. The domain, using the current PC SCOOT Urban Traffic Control system, is assessed through Cognitive Work Analysis (CWA) with the findings used to propose areas suitable for development. One of these areas, STOC validation, is then developed further by applying Ecological Interface Design to develop an alternative display addressing limitations with PC SCOOT’s display. This concept display is then evaluated through two empirical experiments examining performance compared to traditional displays and investigating the role of experience within the domain. Finally, by using insights obtained into the STOC validation process an automated STOC selection algorithm is developed which has the potential to redefine how STOC validation is conducted.

Growing congestion problems of many metropolitan areas which cause excessive traffic delay, instability of travel time generated the need for the development of an Intelligent Vehicle Highway System (IVHS) program that is capable of making significant improvements in mobility, highway safety, and productivity on highways and urban streets. The success of such real time control system highly depends on the new methods that address dynamic traffic assignment. Thus there is an urgent need for an effective dynamic assignment model. The main objective of this research is to emphasize the importance of dynamic traffic assignment for Advanced Driver Information Systems (ADIS) which is one of the major components of IVHS and to present a practical traffic assignment model that is capable of running in real time and which can accurately predict link travel times, queue build up, and network performance. DYNTRAS (DYNamic Traffic Simulation Assignment), a simulation /assignment model is developed. The model uses an incremental loop that assigns a portion of the Origin-Destination matrix, and simulates the movement of the vehicles. Then, it updates travel times and assigns an additional portion of the O-D matrix. In contrast to traditional traffic assignment models like "capacity restraint" and “incremental assignment" techniques that do not consider time dimension, DYNTRAS incorporates time as a third dimension by keeping track of the vehicle movements in time. As a result, it is capable of predicting time-dynamic impacts of congestion and effects of diverted traffic on traffic flow more realistically. The model is applied to a test network. “Several experimental factors are varied to test the sensitivity of the model. The results obtained are presented and general conclusions are derived. The differences between dynamic and static traffic assignment results are also discussed by considering results obtained from both methods. The model needs to be calibrated using real traffic data. According to the results obtained, it needs to be validated. In addition, its long computation time should be reduced to be able to use it for real time applications.<br>Master of Science

Radio Resource Management (RRM) is crucial to efficiently and correctly manage the delivery of quality-of-service (QoS) in IMT-Advanced systems. Various methods on radio resource management for LTE/LTE-Advanced traffic have been studied by researchers especially regarding QoS handling of video packet transmissions. Usually, cross-layer optimisation (CLO) involving the PHY and MAC layers, has been used to provide proper resource allocation and distribution to the entire system. Further initiatives to include the APP layer as part of CLO techniques have gained considerable attention by researchers. However, some of these methods did not adequately consider the level of compatibility with legacy systems and standards. Furthermore, the methods did not wholly address User Equipment (UE) mobility or performance metrics for a specific data type or a specified period. Consequently, in this thesis, a content-aware radio RRM model employing a cross-layer optimiser focusing on a video conferencing/streaming application for a single cell long-term evolution (LTE) system has been proposed. Based on two constructed look-up tables, the cross-layer optimiser was found to dynamically adjust the transmitted video data rates depending on the UE or eNodeB SINR performance. The proposed look-up tables were derived from the performance study of the LTE classical (baseline) simulation model for various distances at a certain UE velocity. Two performance parameters, namely the average throughput and measured SINR were matched together to find the most suitable data rates for video delivery in both the uplink and downlink transmissions. The developed content-aware RRM model was then tested against the LTE baseline simulation model, to benchmark its capability to be used as an alternative to existing RRM methods in the present LTE system. Based on the detailed simulations, the output performance demonstrated that for video packet delivery in both uplink and downlink transmissions, the content-aware RRM model vastly outperformed the legacy LTE baseline simulation model with regard to the packet loss ratio and average end-to-end delay for the same amount of throughput. The baseline simulation model and the newly developed cross-layer approach were investigated and compared with practical measurement results in which PodNode technology, besides other components and supporting simulation software, were used to emulate the LTE communication system. The first emulation experiment involving the baseline model was generally in sync with the uplink throughput simulation performance. The second test which implemented the cross-layer approach employing the look-up table derived from the previous emulated results, confirmed the viability of the proposed content-aware RRM model to be used in current LTE or LTE-Advanced systems for improving the performance in the packet loss ratio and average packet delay.

This thesis is devoted to the study of the optimisation-based control techniques for the design of control strategies that contribute to improve the energy efficiency of smart manufacturing systems. Currently, manufacturing industry is suffering a transformation towards smart, flexible, and energy-efficient manufacturing systems. This transformation requires modularised and reconfigurable manufacturing systems to respond to changes in productions programs and to the time-varying pieces demand while keeping an energy-efficient operation. Thus, suitable control systems should be designed to satisfy the requirements of this transformation while minimising the energy consumption and maximising the plant profit. In this regard, optimisation-based controllers are suitable for the design of control systems that minimise the total energy consumption of such systems while remaining their productivity taking into account the operational conditions and the factors that affect them. First, this dissertation presents how optimisation-based control techniques can contribute to face the challenges of the smart manufacturing systems. Based on this review, manufacturing industry is classified by levels, i.e., machine, process line, and plant levels, for the design of optimisation-based controllers. Besides, to design control strategies that do not affect plant productivity, manufacturing systems are also classified according to the operations performed. Based on these classifications, control strategies are proposed to minimise either the total energy consumption of manufacturing systems or the energy costs related to the operation of such systems. At both machine and process line levels, control strategies are designed based on model predictive control approach to minimise their energy consumption. The underlying idea behind the proposed control strategies consists of managing independently those devices (or systems) that are not directly involved in the machining operations. Thus, energy consumption models are required to predict the total energy consumption profile of manufacturing systems and, based on this, to select the activation/deactivation instants of the manipulated devices that minimise their energy consumption and guarantee their proper operation. Next, due to at the process line level the size and complexity of manufacturing systems increases, a control strategy based on two control modes is proposed to reduce the computational burden of such controllers by switching from a control mode based on online optimisation to an autonomous control mode without solving an optimisation problem. Since the need for flexible and reconfigurable manufacturing systems, non-centralised control strategies are proposed at higher industrial levels to minimise their energy consumption. Thus, both cooperative and non-cooperative local controllers are designed considering a fixed system partitioning and using alternative direction methods of multipliers to solve the optimisations problems in a distributed fashion. Besides, due to the nature of the proposed control objectives, a way to define the consensus stage among the local controllers with coupled dynamics is proposed. Finally, the control strategies designed at plant level are based on the economic model predictive control approach to maximise the plant profit and minimise the operational costs related to the plant operation. At this level, control objectives are focused on determining the economic-optimal production programming of the plant that the control strategies at lower levels should follow. In this regard, the production programming of the plant is determined taking into account the pieces demand, the energy consumption of manufacturing systems, and the current energy market and their fluctuations. All control strategies proposed in this thesis are tested in simulation considering different scenarios designed based on the real operation of an automotive part manufacturing plant.<br>Esta tesis se basa en el estudio de las técnicas de control basadas en optimización para el diseño de estrategias de control que mejoren la eficiencia energética de los sistemas de manufactura inteligentes. La industria de manufactura se está transformando hacia sistemas de manufactura inteligentes, flexibles y eficientes energéticamente, que requiere de estructuras modulares y reconfigurables para poder responder a los cambios en la programación de la producción y la demanda de piezas. Así, se deben diseñar sistemas de control que cumplan los requerimientos de dicha transformación mientras minimizan el consumo de energía y maximizan la rentabilidad de la planta. En este sentido, los controladores basados en optimización son adecuados para el diseño de sistemas de control que minimicen el consumo de energía de dichos sistemas mientras mantienen su productividad teniendo en cuenta los factores que los afectan. Primero, se presentan como las técnicas de control basadas en optimización pueden contribuir a hacer frente a los desafíos impuestos por la industria de manufactura. Con base en esta revisión, la industria manufacturera se clasifica por niveles, nivel de máquina, línea de proceso, y planta, para el diseño de controladores basados en optimización. Además, para diseñar estrategias de control que no afecten la productividad de la planta, se propone una clasificación para estos sistemas en función de las operaciones realizadas. Con base en estas clasificaciones, se diseñan estrategias de control que minimicen el consumo de energía de los sistemas de manufactura o los costos asociados a dicho consumo. A los niveles de maquina y línea, se diseñaron estrategias de control para minimizar el consumo de energía de los sistemas de manufactura con base en el enfoque de control predictivo basado en modelo. Las estrategias propuestas se basan en la gestión independiente de aquellos dispositivos que no están directamente relacionados con las operaciones de mecanizado. Por lo tanto, modelos de consumo de energía fueron necesarios para predecir el perfil del consumo de energía de estos sistemas y, a partir de esto, seleccionar los instantes de activación/desactivación de los dispositivos manipulados que minimicen el consumo de energía y garanticen el correcto funcionamiento de dichos sistemas. Dado que al nivel de línea el tamaño y la complejidad de estos sistemas aumenta, se propone a una estrategia de control basada en dos modos de control para reducir la carga computacional mediante la conmutación de un modo de control basado en optimización a un modo autónomo que no requiere optimización. Dada la necesidad de sistemas de manufactura flexibles y reconfigurables, estrategias de control no centralizadas se proponen para minimizar el consumo de dichos sistemas a los niveles más altos. Para este fin, los sistemas de manufactura se dividieron en subsistemas, y se diseñaron controladores locales de tipo cooperativo y no cooperativo usando métodos alternativos de dirección de multiplicadores para resolver los problemas de optimización. Además, debido a la naturaleza de los objetivos de control propuesto, se propuso una forma de establecer el consenso entre los controladores locales con dinámicas acopladas. Finalmente, a nivel de planta, se diseñan estrategias de control con base en el enfoque control predictivo basado en modelo económico para maximizar la rentabilidad de la planta. A este nivel, los objetivos de control se centran en determinar la programación de la producción óptima que deberán seguir las estrategias de control diseñadas a niveles más bajos. Así, la programación de la producción de la planta se determina teniendo en cuenta la demanda de piezas, el consumo de energía total, y el mercado energético con sus fluctuaciones. Las estrategias de control propuestas en esta tesis se probaron en simulación considerando diferentes escenarios diseñados con base en la operación real de una planta de fabricación de piezas automotrices.<br>Aquesta tesi es centra principalment en l’estudi de les tècniques de control basades en optimització per al disseny d’estratègies que contribueixin a millorar l’eficiència energètica dels sistemes de manufactura intel·ligents. Actualment, la indústria manufacturera està experimentant una transformació cap a sistemes de manufactura intel·ligents, flexibles i eficients energèticament, impulsada pels avenços en dispositius de mesura, gestió de dades i eines de comunicació i connectivitat. Aquesta transformació requereix que els sistemes de manufactura siguin modulars i reconfigurables per poder respondre als canvis en la programació de la producció i de la demanda i disseny de les peces mentre continuen operant de manera eficient i sostenible. Per tant, per tal d’assolir una indústria de manufactura m’és intel·ligent, s’han de dissenyar sistemes de control adequats que permetin complir els requeriments d’aquesta transformació, així com també minimitzar el consum d’energia i maximitzar la rendibilitat de la planta. En aquest sentit, els controladors basats en optimització i les arquitectures de control no centralitzat podrien ser adequats per al disseny de sistemes de control que contribueixin a minimitzar el consum d’energia total d’aquests sistemes mentre mantenen la seva productivitat i tenen en compte les restriccions operatives i els factors externs que afecten aquests sistemes. Per tant, mitjançant l’ús d’estratègies de control avançat, els sistemes de control poden ser degudament actualitzats per incloure la informació sobre els canvis en l’operació dels sistemes de manufactura, així com també la variació del mercat energètic per minimitzar els costos d’energia durant l’operació de la planta. Primer, en aquesta tesi, es presenten i discuteixen les estratègies actualment implementades en la indústria manufacturera per millorar la seva eficiència energètica. En base a aquesta revisió, s’identifiquen les principals bretxes de recerca en aquest camp i es discuteix com les tècniques de control basades en optimització poden contribuir a fer front als desafiaments imposats per la nova era de la indústria manufacturera (Industry 4.0). Recolzant-se en la revisió de la literatura, es proposa classificar la indústria manufacturera per nivells, considerant el nivell de màquina, línia de procés i planta, per al disseny de controladors basats en optimització. A més, per tal de dissenyar estratègies de control que no afectin la productivitat de la planta, és a dir, el nombre de peces processades per unitat de temps, els elements constitutius dels sistemes de manufactura també es classifiquen en dispositius de mecanitzat i perifèrics en funció de les operacions realitzades. Els elements de la primera classe corresponen a aquells que estan directament involucrats en les operacions de mecanitzat, mentre que els de la segona classe són aquells que s’encarreguen de proveir els recursos requerits pels dispositius de mecanitzat. Després, en base a aquesta classificació, es proposen estratègies de control en cada nivell per minimitzar el seu consum d’energia o els costos associats a aquest consum. Per als nivells de màquina i línia de procés, es dissenyen estratègies de control per minimitzar el consum d’energia dels sistemes de manufactura en base a l’enfocament de control predictiu basat en model. Les estratègies proposades es basen en la idea de gestionar de manera independent els dispositius (o sistemes) perifèrics per tal de no afectar el temps de processament de les màquines tot mantenint l’operació dels dispositius de mecanitzat. Per tant, calen models de consum d’energia per a predir el perfil de consum d’energia dels sistemes de manufactura i, en base a aquesta predicció, seleccionar els instants d’activació / desactivació per als dispositius manipulats a partir dels quals es minimitzi el consum d’energia total i es pugui garantir el correcte funcionament d’aquests sistemes. D’altra banda, atès que al nivell de línia de procés la mida i la complexitat dels sistemes de manufactura augmenta, es proposa una estratègia de control basada en dos modes de control per tal de reduir la càrrega computacional i dissenyar controladors que puguin ser implementats en temps real. En aquest sentit, tenint en compte que els sistemes de manufactura presenten un comportament diari, es proposa un algoritme per detectar la periodicitat d’aquests sistemes i, després, commutar a un mode de control autònom que no requereixi resoldre un problema d’optimització en línia. D’altra banda, donada la necessitat de sistemes de manufactura flexibles i reconfigurables, es proposen estratègies de control no centralitzades per minimitzar el consum d’energia dels sistemes de fabricació als nivells més alts. Amb aquesta finalitat, els sistemes de manufactura es divideixen en subsistemes, i es dissenyen controladors locals de tipus cooperatiu i no cooperatiu utilitzant mètodes alternatius de direcció de multiplicadors per resoldre els problemes d’optimització de manera distribuïda. A més, a causa de la naturalesa de l’objectiu de control proposat, el qual està enfocat en minimitzar el consum d’energia dels sistemes de manufactura, es proposa una forma d’establir el consens entre els controladors locals amb dinàmiques acoblades. Després, les estratègies de control proposades són extrapolades al nivell de planta usant objectius de tipus econòmic, i es comparen les arquitectures de control centralitzat i no centralitzat pel que fa al seu acompliment en llac¸ tancat i la càrrega computacional requerida per trobar una solució. Finalment, a nivell de planta, es dissenyen estratègies de control en base a l’enfocament de control predictiu basat en model econòmic per tal de maximitzar la rendibilitat de la planta i minimitzar els costos associats a la seva operació. Per tant, a aquest nivell, els objectius de control se centren a determinar la programació de la producció òptima de la planta que hauran de seguir les estratègies de control dissenyades als nivells més baixos. En aquest sentit, la programació de la producció de la planta és determinada tenint en compte la demanda actual de peces, el consum d’energia dels sistemes de manufactura i el mercat energètic amb les seves fluctuacions. Totes les estratègies de control proposades en aquesta tesi es proven en simulació considerant diferents escenaris basats en l’operació real d’una planta de fabricació de peces automotrius.

The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

Recent years have shown a growing concern over increasing traffic volume worldwide. The insufficient road capacity and the resulting congestions have become major problems in many urban areas. Congestions negatively impact the economy, the environment and the health of the population as well as the drivers satisfaction. Current solutions to this topical and timely problem rely on the exploitation of Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management involves the collection and processing of a large amount of geographically distributed information to control distributed infrastructure and individual vehicles. The distributed nature of the problem prompted the development of a novel, scalable ITS-Cloud platform. The ITS-Cloud organises the processing and manages distributed data sources to provide traffic management methods with more accurate information about the state of the traffic. A new approach to service allocation, derived from the existing cloud and grid computing approaches, was created to address the unique needs of ITS traffic management. The ITS-Cloud hosts the collection of software services that form the Cloud based Traffic Management System (CTMS). CTMS combines intersection control algorithms with intersection approach advices to the vehicles and dynamic routing. The CTMS contains a novel Two-Step traffic management method that relies on the ITS-Cloud to deliver a detailed traffic simulation image and integrates an adaptive intersection control algorithm with a microscopic prediction mechanism. It is the first method able to perform simultaneous adaptive intersection control and intersection approach optimization. The Two-Step method builds on a novel pressure based adaptive intersection control algorithm as well as two new traffic prediction schemes. The developed traffic management system was evaluated using a new microscopic traffic simulation tool tightly integrated with the ITS-Cloud. The novel traffic management approaches were shown to outperform benchmark methods for a realistic range of traffic conditions and road network configurations. Unique to the work was the investigation of interactions between ITS components.

The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.<br>L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupants

This dissertation discusses the growth of auditing and internal control and evaluates the present degree of knowledge and the current and future roles of auditors in a computer-based environment. An analysis of the current state of computer-based auditing is presented along with current research in audit and security methodologies is presented and critiqued. The concept of System Metrics is formulated and defined and a computer-audit analysis system called the Risk Evaluation Model (REM) is created, described and utilized. The Risk Evaluation Model is an interactive set of programs written in FORTRAN which assesses Information Systems for a variety of attributes to judge the "quality" of a system. Currently the system assesses: 1. Portability of the System; 2. Maintainability of the System; 3. Complexity of the System; 4. Known threats to the System and known Features neutralizing those threats; 5. The General System Security Level; and 6. The Hardware Reliability of the System. The model is currently implemented on the Prime 750 computer.

The I-81 Corridor in Virginia traverses the western part of the state, connecting Bristol in the south to Winchester in the north. A study carried out at the Virginia Tech Center for Transportation Research identified traffic safety, work zone safety and traffic control, trucking issues, and intercity traveler information needs as important issues that deserve attention on the I-81 Corridor in Virginia. Analysis of work zone accident statistics showed a need for real-time systems to enhance work zone safety. Real-time advanced warning and traffic control systems provide a means of dynamic information dissemination and advanced warning, thereby enhancing work zone safety and facilitating traffic control. The focus of this research was on the development of functional and system requirements for a real-time advanced warning and traffic control system for work zones. This task was based on the examination of work zone accidents and their causes. The functional requirements include advanced warning, surveillance, advisory, and control functions. Each of these functions consists of several sub-functions. The needs with respect to each of these functions have also been identified. System requirements such as real-time operation, credibility, portability, ease of installation, and adaptability were also identified. Evaluation criteria and potential Measures Of Effectiveness (MOEs) for the evaluation of the system were also identified. Additionally, issues related to the evaluation of the system, such as time duration for evaluation and data collection techniques were identified and examined.<br>Master of Science

In the last decades and due to the Water Framework Directive implementation, the urban wastewater cycle management has become more complex. The concept of integrated urban wastewater system management is introduced, and it becomes necessary to consider some new data as the sewer system characteristics or the receiving body. In this sense, environmental decision support systems (EDSS) are very useful and powerful tools to support during the decision making process. A new EDSS for the integrated urban wastewater system management has been developed integrating data from several sources (bibliographic, theoretical or historical) and a real case-based virtual system able to perform simulations. Results demonstrate the benefits of using this kind of systems in comparison with a standard approach, which do not use the expert knowledge and encourages continuing with this research in order to improve the quality and efficiency of this type of EDSSs<br>En les darreres dècades, i degut a la implementació de la Directiva Marc de l’Aigua, la gestió del cicle urbà de l’aigua ha esdevingut més complexa. S’introdueix el concepte de gestió integrada de conca, i és necessari considerar certa informació, com les característiques de la xarxa de clavegueram o del medi receptor. D’aquesta manera, els sistemes de suport a la decisió ambiental (EDSS) són eines potentíssimes que faciliten la presa de decisions en aquest camp. S’ha desenvolupat un nou EDSS per a la gestió integrada de conca que utilitza una base sòlida de coneixement expert, integrant informació de diverses fonts (bibliogràfiques, teòriques o històriques) i dos sistemes virtuals basats en dades reals sobre els quals és possible realitzar simulacions. Els resultats demostren que aquest sistema presenta beneficis importants respecte a una gestió estàndard sense sistema expert, i esperona a prosseguir amb la recerca i el desenvolupament per a millorar-lo

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

The job of an air traffic controller is stressful by nature. Conditions like aircraft congestion in the skies, an outdated air traffic system and understaffing at control centers can add to the daily stresses of controllers and often cause fatigue. These conditions describe the current status of Air Traffic Services (ATS) in the United States. If left unaddressed, they could compromise safety, the primary objective of air traffic control. The purpose of this thesis is to assess the regulatory framework surrounding ATS specifically in the area of Safety Management Systems (SMS) and Human Factors and determine the course of action to be taken to improve safety in air traffic control.<br>This thesis begins with a description of what air traffic controllers do and the issues that ATS face in the U.S. It then examines the regulatory framework of the International Civil Aviation Organization (ICAO) and the U.S. regarding ATS, specifically SMS and Human Factors. A comparison is made between the privatized air traffic system of Canada and the government owned air traffic system of the U.S. and how privatization makes a difference economically, politically and legally when implementing standards and regulations and enforcing them. Finally, suggestions are made in order to improve the legal framework of air traffic services in the U.S.

The threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will occur. This lack of information posses great challenges on those responsible for security, specifically, on their ability to respond fast, whenever necessary with flexibility and coordination. The surface transportation system plays a critical role in responding to terrorist attacks or other unpredictable human-caused disasters. In particular, existing Intelligent Transportation Systems (ITS) can be enhanced to improve the ability of the surface transportation system to efficiently respond to emergencies and recover from disasters. This research proposes the development of new information technologies to enhance today's ITS with capabilities to improve the crisis response capabilities of the surface transportation system. The objective of this research is to develop a Smart Traffic Evacuation Management System (STEMS) that responds rapidly and effectively to terrorist threats or other unpredictable disasters, by creating dynamic evacuation plans adaptable to continuously changing traffic conditions based on real-time information. The intellectual merit of this research is that the proposed STEMS will possess capabilities to support both the unexpected and unpredictable aspects of a terrorist attack and the dynamic aspect of the traffic network environment. Studies of related work indicate that STEMS is the first system that automatically generates evacuation plans, given the location and scope of an incident and the current traffic network conditions, and dynamically adjusts the plans based on real-time information received from sensors and other surveillance technologies. Refining the plans to keep them consistent with the current conditions significantly improves evacuation effectiveness. The changes that STEMS can handle range from slow, steady variations in traffic conditions, to more sudden variations caused by secondary accidents or other stochastic factors (e.g., high visibility events that determine a sudden increase in the density of the traffic). Being especially designed to handle evacuation in case of terrorist-caused disasters, STEMS can also handle multiple coordinated attacks targeting some strategic area over a short time frame. These are frequently encountered in terrorist acts as they are intended to create panic and terror. Due to the nature of the proposed work, an important component of this project is the development of a simulation environment to support the design and test of STEMS. Developing analytical patterns for modeling traffic dynamics has been explored in the literature at different levels of resolution and realism. Most of the proposed approaches are either too limited in representing reality, or too complex for handling large networks. The contribution of this work consists of investigating and developing traffic models and evacuation algorithms that overcome both of the above limitations. Two of the greatest impacts of this research in terms of science are as follows. First, the new simulation environment developed for this project provides a test bed to facilitate future work on traffic evacuation systems. Secondly, although the models and algorithms developed for STEMS are targeted towards traffic environments and evacuation, their applicability can be extended to other environments (e.g., building evacuation) and other traffic related problems (e.g., real-time route diversion in case of accidents). One of the broader impacts of this research would be the deployment of STEMS in a real environment. This research provides a fundamental tool for handling emergency evacuation for a full range of unpredictable incidents, regardless of cause, origin and scope. Wider and swifter deployment of STEMS will support Homeland Security in general, and will also enhance the surface transportation system on which so many Homeland Security stakeholders depend.<br>Ph.D.<br>School of Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Computer Science

Thesis (Ph. D.)--University of California, Riverside, 2008.<br>Includes abstract. Title from first page of PDF file (viewed February 3, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 122-129). Also issued in print.

Motivated by the desire to explore future traffic flows that will consist of a mixture of classical vehicles and vehicles equipped with advanced driver assistance systems, new mathematical theories and models are developed. The basis for this theory was borrowed from the kinetic description of gas flows, where we replaced the behaviour of the molecules by typical human driving behaviour. From a methodological point of view, this 'human-kinetic' traffic flow theory provides two major improvements with respect to existing theory. Firstly, the model builds exclusively on a mathematical description of individual driver behaviour, whereas traditionally field measurements of traffic flow variables like flow rate and average speed of the flow are needed. This is of major importance for the exploration of future traffic flows with vehicles and equipment that are not yet on the market, and for which at best individual test results from driving simulator experiments or small scale field trials are available. Secondly, the model accounts for the more refined aspects of individual driver behaviour by considering the 'internal' state of the driver (active/passive, aware/unaware,...) and the variations of driving strategy that occur during driving. This is important when the ambition is to capture refined congestion patterns like the occurrence of stop-and-go waves, oscillating congestion and long jams, where the driving strategy may depend for instance on the motivation of the driver to follow closely. This new theory links together the worlds of traffic engineers and behavioural scientists. As such, it is a promising tool that increases the insight in the human behaviour as a basis of various dynamic congestion patterns, and it facilitates the design and evaluation of electronic systems in the vehicle that assist the driver to behave safer, more comfortable and more efficient in busy traffic flows. Herewith, the results of this research are relevant, both for the theoretical interest of the TRAIL research school, and for the more practically oriented work of TNO, who provided financing for this research in the joint T3 research program.

Scheduling and resource allocation is one of the important tasks of the radio resource management layer in long term evolution (LTE) and LTE-Advanced wireless systems. Uplink scheduling and resource allocation is considered more challenging compared to the downlink case because of individual users' power constraints and the discrete nature of spectrum assignment. Downlink scheduling and resource allocation has extensively been studied for relay equipped or heterogeneous traffic networks, but less work has been considered for the LTE uplink case. In this thesis, we have proposed a few uplink scheduling and resource allocation schemes of LTE-Advanced systems that incorporate relay(s) or carry heterogeneous traffic. First, we have proposed a basic uplink scheduling and resource allocation scheme for decode-and-forward relay aided systems. Existing uplink scheduling works have looked at the problem from different angles instead of basic scheduling and resource allocation. In addition of having optimal resource allocation, the proposed scheme is adaptive. If the system has some bad or redundant relays, the proposed scheme can detect and recommends them to be deactivated. Having observed the difficulty in deciding which users to serve for a relay under the constraint of limited power, second, we have proposed a joint source and relay power allocation scheme for an amplify-and-forward relayed system. Existing works of this problem have ignored one term in their problem formulation, and hence failed to offer the optimal solution for all possible scenarios. We have taken care of that missing term in our work, and have shown the performance improvement comparing with the existing works. In this solution, all entities in the network work in an altruistic manner towards maximizing the network capacity. However, in the real world, the nodes may want some benefits while sacrificing their resource. To model the selfish behavior of the nodes, in the third work, we have proposed a game theoretical solution of this problem. Fourth, we have proposed an uplink scheduling and resource allocation scheme for a network which carries heterogeneous traffic. Although there are some existing uplink scheduling works dealing QoS in heterogeneous traffic networks, those were not careful about detailed standard specific all constraints. In addition to meet the conflicting requirements of QoS for different traffic, the proposed scheme takes the resource utilization constraint into account which is designed to benefit the network operators.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate

This research has reviewed the current status of offshore and marine safety. The major problems identified in the research are associated with risk modelling under circumstances where the lack of data or high level of uncertainty exists. This PhD research adopts an object-oriented approach, a natural and straightforward mechanism of organising information of the real world systems, to represent the Offshore Gas Supply Systems (OGSSs) at both the component and system levels. Then based on the object-oriented approach, frameworks of aggregative risk assessment and fault tree analysis are developed. Aggregative risk assessment is to evaluate the risk levels of components, subsystems, and the overall OGSS. Fault trees are then used to represent the cause-effect relationships for a specific risk in the system. Use of these two assessment frameworks can help decision makers to obtain comprehensive view of risks in the OGSS. In order to quantitatively evaluate the framework of aggregative risk, this thesis uses a fuzzy aggregative risk assessment method to determine the risk levels associated with components, subsystems, and the overall OGSS. The fuzzy aggregative risk assessment method is tailored to quantify the risk levels of components, subsystems, and the OGSS. The proposed method is able to identify the most critical subsystem in the OGSS. As soon as, the most critical subsystem is identified, Fuzzy Fault Tree Analysis (FFTA) is employed to quantitatively evaluate the cause-effect relationships for specific undesired event. These results can help risk analysts to select Risk Control Options (RCOs) for mitigating risks in an OGSS. It is not financially possible to employ all the selected RCOs. Therefore, it is necessary to rank and select the best RCO. A decision making method using the Fuzzy TOPSIS (FTOPSIS) is proposed to demonstrate the selection of the best RCOs to control the existing risks in the system. The developed models and frameworks can be integrated to formulate a platform which enables to facilitate risk assessment and safety management of OGSSs without jeopardising the efficiency of OGSSs operations in various situations where traditional risk assessment and safety management techniques cannot be effectively applied.

About 10% of the 60 Petagram (Pg)-Carbon fixed annually through worldwide photosynthetic activity ends up in agricultural residues. Through a heat-induced chemical conversion process such residues can be converted to biochar, a form of carbon that can be employed as a soil amendment, thereby providing long-term storage of carbon in soil. In this application, it has the ability to both reduce GHG emissions and enhance soil structure, moisture and nutrient retention, thereby also addressing global food security issues by improving soil fertility and crop yields.Dealing with several aspects of carbon management and resulting mitigation of GHG emissions, the current project sought to maximize biochar yield from microwave-assisted pyrolysis of maple (Acer L.) wood biomass. Microwave-assisted heating processes are known to be faster and more energy-efficient, yielding higher quality products than conventional methods. Volumetric, spectral and thermodynamic analysis of biochar developed through microwave-assisted pyrolysis showed it to exhibit greater porosity, lower reflectance and greater exothermic energy, and therefore greater overall quality than conventionally-produced biochar. This study also showed this microwave-assisted process to be capable of both producing high quality char and synthesize value-added carbon products. A three-dimensional finite element numerical model developed to optimize the primary parameters was instrumental in optimizing microwave pyrolytic process parameters so as to maximize biochar yields. The influence of selective heating phenomena on pyrolysis conditions was an important factor maximizing biochar yields arising from microwave-assisted pyrolysis of biomass. The application of a doping agent (i.e., microwave receptor) such as char enhanced the severity of the pyrolysis process by better temperature distribution within the biomassBased on numerical models and simulation data, the design of a microwave-assisted pyrolysis reactor affording optimal performance in terms of biochar yields was experimentally validated in a custom-built lab-scale unit. Biochar yield decreased with increasing pyrolysis temperature and time while doping ratio had no significant effect on biochar yields. The maximum predicted yield occurred for an microwave-assisted pyrolysis process optimized at the pyrolysis temperature of 250°C, reaction time of 1 min and doping ratio of 16%.The biochar resulting from microwave-assisted pyrolysis was characterized through various physical and chemical analyses: hyper-spectral imaging, pycnometry, proximate analyses, Scanning Electron Microscopy, Fourier Transform Infrared Radiation and Differential Scanning Calorimetry. The biochar's structural development was directly influenced by the pyrolysis conditions of temperature, residence time and doping ratio.In light of GHG emission balances and the economic feasibility of biochar production, a life cycle analysis was important in estimating the benefits of biochar systems over a wide range of biomass, process and application scenarios. The life cycle analysis determined the sustainability — in terms of reducing the undesired effects of pyrolysis biochar systems — of the proposed process for different types of agricultural residues in Quebec, Canada. This would help farmers to assess the economic vs. environmental benefits of employing this technology to put the agricultural waste they generate to optimal use. The economic viability of the pyrolysis-biochar system was found to be largely dependent on the costs of feedstock production, pyrolysis, and the value of carbon offsets. Therefore, the conclusions drawn from such a life cycle analysis would represent a useful tool in assessing the potential of biochar systems worldwide.<br>Des 60 Petagram (Pg)-C par an fixés à l'échelle mondiale par l'activité photosynthétique, environ 10% demeurent dans les résidus agricoles. Un processus de transformation thermochimique permet de transformer ces résidus en biochar, une forme de carbone (C) dont l'utilisation comme amendement du sol, permet un entreposage du C à long terme, une réduction correspondante des émissions de gaz à effet de serre, ainsi qu'une amélioration de la structure, teneur en eau et rétention d'éléments nutritifs du sol. En améliorant la fertilité des sols et le rendement des cultures, ce procédé s'adresse donc aux enjeux de la sécurité alimentaire mondiale Le présent projet chercha à maximiser le rendement en biochar advenant d'une pyrolyse de biomasse de bois d'érable (Acer L.). Offrant des produits d'une qualité supérieur à celle des procédés conventionnels, le chauffage assisté par micro-ondes est parmi les procédés les plus rapides et écoénergétiques. Les analyses volumétriques, spectrales et thermodynamiques du biochar provenant d'une pyrolyse assistée par micro-ondes (PAM) démontrèrent sa plus grande porosité, son coefficient de réflexion moins élevé et sa plus grande énergie exothermique, et donc sa plus grande qualité globale par rapport au biochar d'origine conventionnelle. La PAM a produit un biochar de haute qualité tout en synthétisant des composés de carbone à valeur ajoutée. Une modélisation tridimensionnelle par éléments finis visant à optimiser les paramètres du procédé de PAM s'avéra clé à l'optimisation du rendement en biochar. L'influence de phénomènes d'échauffement sélectif sur les conditions de pyrolyse s'avéra un facteur important dans l'optimisation du rendement en biochar produit par PAM de biomasse. L'ajout d'un agent dopant (i.e., matériel récepteur des micro-ondes) tel que du biochar, a réduit la sévérité du procédé de PAM en permettant une meilleure distribution de la température à travers la biomasse.Grâce à des modélisations numériques et des données de simulation, la conception d'un réacteur pyrolytique assisté par micro-ondes, permettant d'obtenir une performance optimale quant au rendement de biochar, fut validée expérimentalement dans un appareil bâti sur mesure à l'échelle du laboratoire. Face à une augmentation de la température de pyrolyse, le rendement en biochar diminua, mais la fraction d'agent de dopage n'eut aucun effet significatif sur celui-ci. Le rendement maximum prévu pour le procédé de PAM fut obtenu avec une température de pyrolyse de 250°C, un temps de réaction de 1 min, et une fraction d'agent de dopage de 16%.Diverses analyses physiques et chimiques de biochar provenant d'une PAM servirent à sa caractérisation: imagerie hyperspectrale, pycnométrie, analyses immédiates, microscopie à balayage électronique, spectrométrie infrarouge à transformée de Fourier, analyse calorimétrique différentielle. La structure du biochar fut influencée par les conditions de pyrolyse (e.g., température, temps de séjour, coefficient de dopage).Une analyse du cycle de vie s'avéra importante à l'évaluation des avantages des systèmes de biochar pour une variété de biomasses, procédés de pyrolyse et taux d'amendement. Cette analyse évalua, pour différents résidus agricoles du Québec, le caractère durable du procédé proposé, particulièrement vis-à-vis la réduction des effets néfastes de la production de biochar par pyrolyse. Cette analyse permettra aux agriculteurs d'évaluer les avantages économiques vs. environnementaux de cette technologie pour la mise en valeur optimale des résidus agricoles. La viabilité économique du système de production de biochar par PAM se trouva principalement liée aux coûts de production des matières premières, de pyrolyse, et de la valeur des crédits d'émission de carbone. Les conclusions d'une telle analyse représentent donc un outil d'une grande valeur dans l'évaluation du potentiel du biochar à l'échelle mondiale.

International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California<br>The Consultative Committee for Space Data Systems (CCSDS) is an international organization chartered to develop and adopt communications protocols and data processing standards suitable for use in space-related communication and data processing systems. Among its ongoing activities, CCSDS is in the final stages of approving recommendations for communications services and protocols to be used by "advanced orbiting systems" (AOS) typified by the international Space Station Freedom Program. As a companion activity to the development of the AOS protocols, CCSDS is in the process of defining the network management functions that are necessary to realize the full capabilities of the AOS services. CCSDS is also concerned with communicating management information among distributed network management systems, an environment that will be typical of multi-national space ventures. These network management systems are envisioned to be automated, resulting in a need for standard network management protocols and data structures. This paper briefly describes the CCSDS network management environment, and reviews the current status of CCSDS recommendations for network management functional capability, use of international standards for network management, and composition of signalling systems in support of the AOS services. The paper concludes with a timetable for future work in this area.