This paper studies the impacts on flight trajectories, such as lateral and vertical changes, when avoiding the formation of persistent contrails for transatlantic flights. A sophisticated Earth-System Model (EMAC) coupled with a flight routing submodel (AirTraf) and a contrail submodel (CONTRAIL) is used to optimize flight trajectories concerning the flight time and the flight distance through contrail forming regions (contrail distance). All the trajectories are calculated taking into account the effects of the actual and local meteorological parameters, e.g., wind, temperature, relative humidity, etc. A full-year simulation has been conducted based on a daily flight schedule of 103 transatlantic flights. The trade-off between the flight time and contrail distance shows a large daily variability, meaning for the same increase in flight time, the reduction in contrail distance varies from 20% to 80% depending on the daily meteorological situation. The results confirm that the overall changes in flight trajectories follow a seasonal cycle corresponding to the nature of the potential contrail coverage. In non-summer seasons, the southward and upward shifts of the trajectories are favorable to avoid the contrail formation. In summer, the northward and upward shifts are preferred. A partial mitigation strategy for up to 40% reduction in contrail distance can be achieved throughout all the seasons with a negligible increase in flight time (less than 2%), which represents a reasonable trade-off between flight time increase and contrail avoidance.

Intelligent transportation systems involve a variety of information and control systems methodologies, from cooperative systems which aim at traffic flow optimization by means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, to information fusion from multiple traffic sensing modalities. This thesis aims to address three problems in intelligent transportation systems, one in optimal control of connected automated vehicles, one in discrete-event and hybrid traffic simulation model, and one in sensing and classifying roadway obstacles in smart cities. The first set of problems addressed relates to optimally controlling connected automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. A closed-form analytical solution is derived while taking speed, control, and safety constraints into consideration. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration. The framework is capable of accommodating for turns and ensures the absence of collisions. In the meantime, a measurement of passenger comfort is taken into account while the vehicles make turns. In addition to the first-in-first-out (FIFO) ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This thesis also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic. To validate the effectiveness of the proposed solution, a discrete-event and hybrid simulation framework based on SimEvents is proposed, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at the microscopic level, including new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays. The framework spans multiple toolboxes including MATLAB, Simulink, and SimEvents. In another direction, an unsupervised anomaly detection system is developed based on data collected through the Street Bump smartphone application. The system, which is built based on signal processing techniques and the concept of information entropy, is capable of generating a prioritized list of roadway obstacles, such that the higher-ranked entries are most likely to be actionable bumps (e.g., potholes) requiring immediate attention, while those lower-ranked are most likely to be nonactionable bumps(e.g., flat castings, cobblestone streets, speed bumps) for which no immediate action is needed. This system enables the City to efficiently prioritize repairs. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of the system in practice.

Constrained optimization is used to derive human postures and movements. In the first study a static 3D model with 30 muscle groups is used to analyse postures. The activation levels of these muscles are minimized in order to represent the individual's choice of posture. Subject specific data in terms of anthropometry, strength and orthopedic aids serve as input. The aim is to study effects from orthopedic treatment and altered abilities of the subject. Initial validation shows qualitative agreement of posture strategies but further details about passive stiffness and anthropometry are needed, especially to predict pelvis orientation. In the second application, the athletic long jump, a problem formulation is developed to find optimal movements of a multibody system when subjected to contact. The model was based on rigid links, joint actuators and a wobbling mass. The contact to the ground was modelled as a spring-damper system with tuned properties. The movement in the degrees of freedom representing physical joints was described over contact time through two fifth-order polynomials, with a variable transition time, while the motion in the degrees of freedom of contact and wobbling mass was integrated forwards in time, as a consequence. Muscle activation variables were then optimized in order to maximize ballistic flight distance. The optimization determined contact time, end configuration, activation and interaction with the ground from an initial configuration. The results from optimization show a reasonable agreement with experimentally recorded jumps, but individual recordings and measurements are needed for more precise conclusions.

Connected aviation, the Internet of Flying Things and related emerging technologies, such as the System-Wide Information Management infrastructure of the FAA NextGen program, present numerous opportunities for the aviation sector. The ubiquity of aeronautical, flight, weather, aerodrome, and maintenance data accelerates the development of smarter software systems to cope with the ever increasing requirements of the industry sector. The increasing amount, frequency and variety of real-time data available to modern air transport and tactical systems, and their crews, creates exciting new challenges and research opportunities. We present an architectural approach toward the vision of increasingly self-separating and self-governed flight operations within the bigger picture of an evolving set of future Autonomous Flight Rules. The challenges in this field of research are manifold and include autonomic airborne trajectory optimization, data sharing, fusion and information derivation, the incorporation of and communication with rational actors—both human and machine—via a connected aviation infrastructure, to facilitate smarter decision making and support while generating economical, environmental and tactical advantages. We developed a concept and prototype implementation of our Smart Autoflight Control System. The concept and implemented system follow the design principle of an Autonomic Element, consisting of an Autonomic Manager and its Managed Element, acting within an Autonomic Context. The Managed Element concept embraces an infrastructure featuring suitable models of manageable environments, airborne agents, planners, applicable operational cost and risk policies, and connections to the System-Wide Information Management cloud as well as to relevant rational actors, such as Air Traffic Control, Command and Control, Operations or Dispatch. The Autonomic Manager concept incorporates the extraction, that is, short-term sensing, of features from operational scenarios and the categorization of these scenarios according to their level of criticality and associated flight phase. The Autonomic Manager component, furthermore, continuously tunes, that is, actuates, manageable items of its Managed Element, such as environments and planners, and triggers competitions to assess their performance under the various extracted and dynamically changing features of their Autonomic Context. The performance reputations of the tuned manageable items are collected in a knowledge base and may serve as a long-term sensor. Both the managed items of the Managed Element as well the managing items of the Autonomic Manager are extendable and may realize very different paradigms, including deterministic, non-deterministic, heuristically guided, and biologically inspired approaches. We assessed the extensibility and maintainability of our Smart Autoflight Control System infrastructure by including manageable environments and planners of the Classical Grid Search, Probabilistic Roadmaps, and Rapidly-Exploring Random Trees families into its core component. Furthermore, we evaluated the viability of a simple heuristic and a more sophisticated Sequential Model-Based Algorithm Configuration Autonomic Manager to adaptively select and tune manageable planners of the supported families based on the extracted features from very simple to highly challenging scenarios. We were able to show that a self-adaptive approach, that heuristically tunes and selects the best performing planner following a performance competition, produces suitable flight trajectories within reasonable deliberation times. Additionally, we discovered options for improving our heuristic Autonomic Manager through a series of evaluation runs of the Sequential Model-Based Algorithm Configuration Autonomic Manager. Our contributions answer how the manageable items, that is, environments and planners, of our Smart Autoflight Control System core component have to be modified in order to embed System-Wide Information Management data that feature both spatial and temporal aspects. We show how operational cost and risk policies help to assess environments differently and plan suitable flight trajectories accordingly. We identify and implement the necessary extensions and capabilities that have to be supported by manageable and managing items, respectively, to enable continuous feature extraction, adaptive tuning, performance competitions, and planner selection in dynamic flight scenarios. / Graduate

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The development of connected vehicle (CV) technologies enables advanced management of individual vehicles and traffic signals to improve urban mobility and energy efficiency. In this thesis, a cooperative vehicle-signal control system will be developed to integrate an Eco-driving system and a proactive signal control system under a mixed connected environment with both connected vehicles (CVs) and human-driven vehicles (HDVs). The system utilizes CVs to conduct an accurate prediction of queue length and delay at different approaches of intersections. Then, a queue-based optimal control strategy is established to minimize the fuel usage of individual CVs and the travel time delay of entire intersections. The system applies the model predictive control to search for the optimal signal timing plan for each intersection and the most-fuel efficient speed profiles for each CV to gain the global optimum of the intersection. In this thesis, a simulation platform is designed to verify the effectiveness of the proposed system under different traffic scenarios. The comparison with the eco-driving only and signal control only algorithms verifies that the cooperative system has a much more extensive reduction range of the trip delay in the case of medium and high saturation. At low saturation, the effect of the system is not much different from that of the eco-driving algorithm, but it is still better than the signal control. Results show that the benefits of CVs are significant at all different market penetration rates of CVs. It also demonstrates the drawback of the system at high congestion levels. / Thesis / Master of Applied Science (MASc)

One of the biggest challenges associated with optimization based methods forrobotic motion planning is their extreme sensitivity to a good initial guess,especially in the presence of local minima in the cost function landscape.Additional challenges may also arise due to operational constraints, robotcontrollers sometimes have very little time to plan a trajectory to perform adesired function. To work around these limitations, a common solution is tosplit the motion planner into an offline phase and an online phase. The offlinephase entails computing reference trajectories for varying parameterizationsof the task space in the form of a lookup table. During the online phase,a stripped down version of the optimizer is supplied with a suitable initialguess from the lookup table using the current state estimate of the robot andits surrounding bodies. This method helps in alleviating problems related toboth local minima and operational time constraints, by seeding the optimizerwith a suitable initial guess that allows it to converge to the global minimummuch faster.The problem however, shifts to the computational complexity of computinga lookup table of reference trajectories for a fine enough discreti- zation ofthe input state space. For many robotic scenarios of interest, it is oftenimpractical and sometimes computationally infeasible to compute a look uptable using a serial, single core implementation of the offline phase of a motionplanner. The main contribution of this work is to develop and evaluate amethod for reducing the time spent on computing a lookup table of referencetrajectories during the offline phase of motion planners based on optimalcontrol. We implement a method to offload the computation of collisionavoidance constraints during trajectory optimization on a Graphics ProcessingUnit (GPU), while simultaneously benefiting from a task based approach todistribute lookup table computations for independent subsets of the input statespace across multiple processes on a cluster of machines. We demonstrate theefficacy of the proposed method in a practical setting by implementing andevaluating it within a representative motion planner based on optimal control.We observe that the implemented method is 115x faster than the originalserial version of the planner, using 86 processes on 5 machines with standardserver grade hardware and 5 Graphics Processing Units in total. Additionally,we observe that the implemented method results in solutions identical to theoriginal serial version in 96.6% of cases, lending credibility for its use inrobotic motion planning. / En av de största utmaningarna med optimeringsbaserade metoder för rörelseplaneringinom robotik är deras extrema känslighet för en bra initial gissning,särskilt i närvaro av lokala minima i kostnadsfunktionslandskapet. Ytterligareutmaningar kan också uppstå på grund av operativa begränsningar. Robotkontrollerhar ibland väldigt lite tid att planera en väg för att utföra en önskadfunktion. För att kringgå dessa begränsningar är en vanlig lösning att dela upprörelseplaneraren i en offline-fas och en online-fas. Offlinefasen inkluderarberäkning av referensvägar för olika punkter i ingångstillståndsutrymmet iform av en uppslagstabell. Under online-fasen levereras en avskalad versionav optimeraren med en lämplig initial gissning från uppslagstabellen medden aktuella uppskattningen av roboten och dess omgivande kroppar. Dennametod hjälper till att lindra problem relaterade till både lokala minima ochdriftstidsbegränsningar genom att sådd optimeraren med en lämplig initialgissning som gör att den kan konvergera till det globala minimumet mycketsnabbare.Problemet flyttas emellertid nu till beräkningskomplexiteten för att beräknaen uppslagstabell över referensvägar för ett tillräckligt fint utrymme för ingångstillståndsutrymmet.För många robotscenarier av intresse är det ofta opraktisktoch ibland beräkningsmässigt omöjligt att beräkna en uppslagstabell med hjälpav en seriell, enda kärnimplementering av offline-fasen i en rörelseplanner.Huvudbidraget till detta arbete är att utveckla och utvärdera en metod för attminska tiden som används för att beräkna en uppslagstabell över referensvägarunder offline-fasen för rörelsesplanerare baserat på optimal kontroll. Vi implementeraren metod för att utföra en kollision undvika en grafikbehandlingsenhet(GPU), medan du använder en uppgiftsbaserad metod för att distribuerauppslagningsberäkningar för oberoende delmängder av inmatningsutrymmeöver flera processer i ett kluster av maskiner. Vi demonstrerar effektivitetenav den föreslagna metoden i en praktisk miljö genom att implementeraoch utvärdera den inom en representativ rörelseplanner baserat på optimalkontroll. Vi noterar att den implementerade metoden är 115 gånger snabbareän den ursprungliga serieversionen av schemaläggaren, med 86 processer på 5maskiner med standardhårdvara och totalt 5 GPU: er. Dessutom observerarvi att den implementerade metoden resulterar i lösningar som är identiskamed den ursprungliga serieversionen i mer än 96,6 % av fallen, vilket gertrovärdighet för dess användning i robotrörelse planering.

This thesis consists of four chapters. In Chapter 1, we focus on a class of transportation inequalities known as the transportation-information inequalities. These inequalities bound optimal transportation costs in terms of relative Fisher information, and are known to characterize certain concentration properties of Markov processes around their invariant measures. We provide a characterization of the quadratic transportation-information inequality in terms of a dimension-free concentration property for i.i.d. copies of the underlying Markov process, identifying the precise high-dimensional concentration property encoded by this inequality. We also illustrate how this result is an instance of a general convex-analytic tensorization principle. In Chapter 2, we study the entropic gradient flow property of McKean--Vlasov diffusions via a stochastic analysis approach. We formulate a trajectorial version of the relative entropy dissipation identity for these interacting diffusions, which describes the rate of relative entropy dissipation along every path of the diffusive motion. As a first application, we obtain a new interpretation of the gradient flow structure for the granular media equation. Secondly, we show how the trajectorial approach leads to a new derivation of the HWBI inequality. In Chapter 3, we further extend the trajectorial approach to a class of degenerate diffusion equations that includes the porous medium equation. These equations are posed on a bounded domain and are subject to no-flux boundary conditions, so that their corresponding probabilistic representations are stochastic differential equations with normal reflection on the boundary. Our stochastic analysis approach again leads to a new derivation of the Wasserstein gradient flow property for these nonlinear diffusions, as well as to a simple proof of the HWI inequality in the present context. Finally, in Chapter 4, we turn our attention to mean field approximation -- a method widely used to study the behavior of large stochastic systems of interacting particles. We propose a new approach to deriving quantitative mean field approximations for any strongly log-concave probability measure. Our framework is inspired by the recent theory of nonlinear large deviations, for which we offer an efficient non-asymptotic perspective in log-concave settings based on functional inequalities. We discuss three implications, in the contexts of continuous Gibbs measures on large graphs, high-dimensional Bayesian linear regression, and the construction of decentralized near-optimizers in high-dimensional stochastic control problems.