Traffic information, now available through a number of different sources, is re-shaping the way planners, operators and users think about the transportation network. It provides a powerful tool to mitigate the negative impacts of uncertainty, and an invaluable resource to manage and operate the network in real-time. More information also invites to think about traditional transportation problems from a different perspective, searching for a better utilization of the improved knowledge of the network state. This dissertation is concerned with modeling and evaluating the system-level impacts of providing information to network users, assuming that the data is utilized to guide an Adaptive System-Optimum (ASO) routing behavior. Within this context, it studies the optimal deployment of sensors for the support of ASO strategies, and it introduces a novel SO assignment approach, the Information-Based System Optimum (IBSO) assignment paradigm. The proposed sensor deployment model explicitly captures the impact of sensors' location on the expected cost of ASO assignment strategies. Under such strategies, a-priori routing decisions may be adjusted based on real-time information. The IBSO assignment paradigm leads to optimal flow patterns which take into account the ability of vehicles to collect information as they travel. The approach regards a subset of the system's assets as probes, which may face higher expected costs than regular vehicles in the search for information. The collected data is utilized to adjust routing decisions in real time, improving the expected system performance. The proposed problem captures the system-level impact of adaptive route choices on stochastic networks. The models developed in this work are rigorously formulated, and their properties analyzed to support the generation of specialized solution methodologies based on state-space partitioning and Tabu Search principles. Solution techniques are tested under a variety of scenarios, and implemented to the solution of several case studies. The magnitude and nature of the information impacts observed in this study illustrate problem characteristics with important theoretical, methodological and practical implications. The findings presented in this dissertation allow envisioning a number of practical applications which may promote a more efficient utilization of novel sensing and communication technologies, allowing the full realization of their potential. / text

This dissertation is exploratory in design and employs an electronic survey and comparative case studies to examine the factors that shape the impact of a non-traditional data source that measures the market power of urban neighborhoods, the Social Compact Neighborhood Market DrillDown, on the policymaking process of local government officials concerned with neighborhood economic development. The four case studies are: Baltimore, MD, Louisville, KY, Detroit, MI, and Tampa, FL. The study examines the conditions that affect decision-making at the different stages of information use and considers instrumental, conceptual, and symbolic uses of information. The observation of seven variables (applicability to agenda of lead sponsor, congruence between findings and prior preferences, trust of information producer, availability of alternative information sources, information sustainability, costs of production, information as private sector "lure") provide the context for theory and hypotheses on information impact in which three factors are found to be significant (applicability to agenda to lead sponsor, information sustainability, and information as private sector "lure"). Overall, the study finds evidence that information use is inherently a political endeavor in which its use is dominated by the preferences of those who sponsor its production and use information toward initiatives that are important to them. / Political Science

Les premières recherches en détection, localisation et séparation de signaux remontent au début du 20ème siècle. Ces recherches sont d’actualité encore aujourd’hui, notamment du fait de la croissance rapide des systèmes de communications constatée ces deux dernières décennies. Par ailleurs, la littérature du domaine consacre très peu d’études relatives à certains contextes jugés difficiles dont certains sont traités dans cette thèse. Ce travail porte sur la localisation de signaux par détection des temps d’arrivée ou estimation des directions d’arrivée et sur la séparation de sources dépendantes ou à module constant. L’idée principale est de tirer profit de certaines informations a priori disponibles sur les signaux sources telles que la parcimonie, la cyclostationarité, la non-circularité, le module constant, la structure autoregressive et les séquences pilote dans un contexte coopératif. Une première partie détaille trois contributions : (i) un nouveau détecteur pour l’estimation des temps d’arrivée basé sur la minimisation de la probabilité d’erreur ; (ii) une estimation améliorée de la puissance du bruit, basée sur les statistiques d’ordre ; (iii) une quantification de la précision et de la résolution de l’estimation des directions d’arrivée au regard de certains a priori considérés sur les sources. Une deuxième partie est consacrée à la séparation de sources exploitant différentes informations sur celles-ci : (i) la séparation de signaux de communication à module constant ; (ii) la séparation de sources dépendantes connaissant la nature de la dépendance et (iii) la séparation de sources autorégressives dépendantes connaissant la structure autorégressive. / Signal detection, localization, and separation problems date back to the beginning of the twentieth century. Nowadays, this subject is still a hot topic receiving more and more attention, notably with the rapid growth of wireless communication systems that arose in the last two decades and it turns out that many challenging aspects remain poorly addressed by the available literature relative to this subject. This thesis deals with signal detection, localization using temporal or directional measurements, and separation of dependent source signals. The main objective is to make use of some available priors about the source signals such as sparsity, cyclo-stationarity, non-circularity, constant modulus, autoregressive structure or training sequences in a cooperative framework. The first part is devoted to the analysis of (i) signal’s time-of-arrival estimation using a new minimum error rate based detector, (ii) noise power estimation using an improved order-statistics estimator and (iii) side information impact on direction-of-arrival estimation accuracy and resolution. In the second part, the source separation problem is investigated at the light of different priors about the original sources. Three kinds of prior have been considered : (i) separation of constant modulus communication signals, (ii) separation of dependent source signals knowing their dependency structure and (iii) separation of dependent autoregressive sources knowing their autoregressive structure.