Dynamic multi-ramp metering control with simultaneous perturbation stochastic approximation (SPSA)

Ramp metering was proven to be a viable form of freeway traffic control strategy, which could eliminate, or at least reduce, freeway congestion. In this study, the development of ramp metering control strategies, models, and constraints (e.g., meter locations, ramp storage capacities, lower and upper bounds of ramp metering rates) are discussed in detail. The pre-timed and demand/capacity metering control strategies were first evaluated, while the potential metered ramps were determined. A Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is proposed to dynamically optimize multiple-ramp metering control by maximizing the total throughput subject to a number of constraints. The ramp metering rates subject to dynamic traffic conditions and capacity constraints are considered as decision variables in the SPSA algorithm. Based on the collected geometric and traffic data, a CORSIM model was developed to simulate traffic operation for the study site. The potential benefit of the dynamic multi-ramp metering control model under time varying traffic condition was simulated and evaluated. The increased total throughput and reduced total delay were observed, while the traffic conditions suitable for implementing ramp metering control were suggested. The developed dynamic multi-ramp metering control with SPSA algorithm has demonstrated its effectiveness to improve freeway operation

Satellite Orbit Determination Using Payload-Collected Observation Data

The algorithm developed in this research provides a novel way of preparing the space industry for the future. The rapid rise in small satellite deployment and miniaturization of communication technology will require a cheaper, leaner and more efficient way of tracking those satellites. Using the already-collected timing data from the payload observations means that no additional on-board equipment or processing will be required and that it could even be applied to existing missions, as well. This thesis provides a solid foundation and development analysis to support this new way of using satellite payload data. It shows how combining even the most basic form of observed data (the time-of-access and location) can provide deeper and more insightful knowledge. Each cost function used and explored combines this data in a different manner and, therefore, provides a different kind of insight pertaining to a different aspect of the satellite behaviour. This, combined with the power of machine learning, has proven to be an effective way of determining the position and velocity of the satellite with strong potential for future development in real-world Earth-observation or, perhaps even, interplanetary missions

Stochastic System Design and Applications to Stochastically Robust Structural Control

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. In this framework, the design objective is typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. This system design process is called stochastic system design and the associated design optimization problem stochastic optimization. In this thesis general stochastic system design problems are discussed. Application of this design approach to the specific field of structural control is considered for developing a robust-to-uncertainties nonlinear controller synthesis methodology. Initially problems that involve relatively simple models are discussed. Analytical approximations, motivated by the simplicity of the models adopted, are discussed for evaluating the system performance and efficiently performing the stochastic optimization. Special focus is given in this setting on the design of control laws for linear structural systems with probabilistic model uncertainty, under stationary stochastic excitation. The analysis then shifts to complex systems, involving nonlinear models with high-dimensional uncertainties. To address this complexity in the model description stochastic simulation is suggested for evaluating the performance objectives. This simulation-based approach addresses adequately all important characteristics of the system but makes the associated design optimization challenging. A novel algorithm, called Stochastic Subset Optimization (SSO), is developed for efficiently exploring the sensitivity of the objective function to the design variables and iteratively identifying a subset of the original design space that has v i high plausibility of containing the optimal design variables. An efficient two-stage framework for the stochastic optimization is then discussed combining SSO with some other stochastic search algorithm. Topics related to the combination of the two different stages for overall enhanced efficiency of the optimization process are discussed. Applications to general structural design problems as well as structural control problems are finally considered. The design objectives in these problems are the reliability of the system and the life-cycle cost. For the latter case, instead of approximating the damages from future earthquakes in terms of the reliability of the structure, as typically performed in past research efforts, an accurate methodology is presented for estimating this cost; this methodology uses the nonlinear response of the structure under a given excitation to estimate the damages in a detailed, component level

Integrated Hydraulic Fracture Placement and Design Optimization in Unconventional Gas Reservoirs

Unconventional reservoir such as tight and shale gas reservoirs has the potential of becoming the main source of cleaner energy in the 21th century. Production from these reservoirs is mainly accomplished through engineered hydraulic fracturing to generate fracture networks that provide the gas flow pathways from the rock matrix to the production wells. While hydraulic fracturing technology has progressed considerably in the last thirty years, designing the fracturing system primarily involves judgments from a team of engineers, geoscientists and geophysicists, without taking advantage of computational tools, such as numerical optimization techniques to improve short-term and long-term reservoir production. This thesis focuses on developing novel optimization algorithms that can be used to improve the design and implementation of hydraulic fracturing in a shale gas reservoir to increase production and the net present value of unconventional assets. In particular, we consider simultaneous perturbation stochastic approximation (SPSA) and Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) algorithms, which are proven very efficient in finding nearly optimal solutions. We show that with a judicious choice of control variables (continuous or discrete) we can obtain efficient algorithms for performing hydraulic fracture optimization in unconventional reservoirs. To achieve this, the hydraulic fracture production optimization problem is divided into two aspects: fracture stages placement optimization with fix stage numbers and unknown stage numbers. After check the parameters of fracture model that could be used to simulate future reservoir behavior with a higher degree of confidence, the fracture stages optimization is scheduling the fracturing sequence, and adjusting the fracture stages intensity at different locations, which is similar to well placement problem. In addition to the detailed investigation of the new optimization technique, uncertainty quantification of reservoir properties and its implications on the optimization workflow is also considered in the shale gas reservoir model. Taking into account that shale gas reservoirs are highly heterogeneous systems, stochastic optimization methods are the most suitable framework for hydraulic fracture stages placement

Simulation-based optimal Bayesian experimental design for nonlinear systems

The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models; in particular, we focus on finding sets of experiments that provide the most information about targeted sets of parameters. Our framework employs a Bayesian statistical setting, which provides a foundation for inference from noisy, indirect, and incomplete data, and a natural mechanism for incorporating heterogeneous sources of information. An objective function is constructed from information theoretic measures, reflecting expected information gain from proposed combinations of experiments. Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. These algorithms are demonstrated on model problems and on nonlinear parameter estimation problems arising in detailed combustion kinetics.Comment: Preprint 53 pages, 17 figures (54 small figures). v1 submitted to the Journal of Computational Physics on August 4, 2011; v2 submitted on August 12, 2012. v2 changes: (a) addition of Appendix B and Figure 17 to address the bias in the expected utility estimator; (b) minor language edits; v3 submitted on November 30, 2012. v3 changes: minor edit

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

Analyzing SPSA approaches to solve the non-linear non-differentiable problems arising in the assisted calibration of traffic simulation models

Mathematical and simulation models of systems lay at the core of decision support systems, and their role become more critical as more complex is the system object of decision. The decision process usually encompasses the optimization of some utility function that evaluates the performance indicators that measure the impacts of the decisions. An increasing difficulty directly related to the complexity of the system arises when the associated function to be optimized is a not analytical, non-differentiable, non-linear function which can only be evaluated by simulation. Simulation-Optimization techniques are especially suited in these cases and its use is increasing in traffic models, an archetypic case of complex, dynamic systems exhibiting highly stochastic characteristics. In this approach simulation is used to evaluate the objective function, and a non-differentiable optimization technique to solve the optimization problem is used. Simultaneous Perturbation Stochastic Approximation (SPSA) is one of the most popular of these techniques. This thesis analyzes, discusses and presents computational results for the application of this technique to the calibration of a traffic simulation model of a Swedish highway section. Variants of the SPSA, replacing the usual gradient approach by a combination of normalized parameters and penalized objective function, have been proposed in this study due to an exhaustive analysis of the behavior of classical SPSA where problems arose from different magnitude variables. In this work, a varied set of Software environments have been used, combining RStudio for the analysis, Python and MATLAB for the SPSA implementation, AIMSUN as a Traffic Model Simulator, and SQLite for obtaining of simulated data and Tableau for visualizing data and results

ECG based Prediction Model for Cardiac-Related Diseases using Machine Learning Techniques

This dissertation presents research on the construction of predictive models for health conditions through the application of Artificial Intelligence methods. The work is thus focused on the prediction, in the short and long term, of Atrial Fibrillation conditions through the analysis of Electrocardiography exams, with the use of several techniques to reduce noise and interference, as well as their representation through spectrograms and their application in Artificial Intelligence models, specifically Deep Learning. The training and testing processes of the models made use of a publicly available database. In its two approaches, predictive algorithms were obtained with an accuracy of 96.73% for a short horizon prediction and 96.52% for long Atrial Fibrillation prediction horizon. The main objectives of this dissertation are thus the study of works already carried out in the area during the last decade, to present a new methodology of prediction of the presented condition, as well as to present and discuss its results, including suggestions for improvement for future development.Esta dissertação descreve a construção de modelos preditivos de condições de saúde através de aplicação de métodos de Inteligência Artificial. O trabalho é assim focado na predição, a curto e longo prazo, de condições de Fibrilhação Auricular através da análise de exames de Eletrocardiografia, com a utilização de diversas técnicas de redução de ruído e de interferência, bem como a sua representação através de espectrogramas e sua aplicação em modelos de Inteligência Artificial, concretamente de Aprendizagem Profunda (Deep Learning na língua inglesa). Os processos de treino e teste dos modelos obtidos recorreram a uma base de dados publicamente disponível. Nas suas duas abordagens, foram obtidos algoritmos preditivos com uma precisão de 96.73% para uma predição de curto horizonte e 96.52% para longo horizonte de predição de Fibrilhação Auricular. Os objetivos principais da presente dissertação são assim o estudo de trabalhos já realizados na área durante a última década, apresentar uma nova metodologia de predição da condição apresentada, bem como apresentar e discutir os seus resultados, incluindo sugestões de melhoria para futuro desenvolvimento

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set