Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

Depuis une dizaine d'années, on observe un développement de l'électronique embarquée intégrée à la plupart des objets que nous utilisons au quotidien. Il s'agit maintenant de les interconnecter en créant des réseaux embarqués connectés tels que les réseaux de capteurs autonomes sans fils. La miniaturisation des composants permet d'envisager une autonomie énergétique de ces réseaux composés de capteurs, récupérateurs d'énergie et de micro-batteries. Cependant la faible durée de vie des batteries et leur puissance limitée sont problématiques pour de telles applications. Les micro-supercondensateurs représentent une alternative pertinente pour la gestion de l'énergie dans les systèmes embarqués, notamment grâce à leur durée de vie très élevée. L'objectif de cette thèse concerne l'optimisation des performances de ces dispositifs en termes de densité de puissance et d'énergie. La capacité du supercondensateur étant proportionnelle à la surface électrochimiquement active des électrodes, nous nous sommes donc intéressés à la structuration de collecteurs de courant en or pour optimiser les performances des micro-supercondensateurs à base d'oxyde de ruthénium. Nous avons sélectionné deux principales techniques pour fabriquer une structure tridimensionnelle de l'or. Dans un premier temps, le dépôt physique d'or par évaporation à angle oblique (OAD) nous a permis de réaliser un substrat colonnaire suivi d'un dépôt d'oxyde de ruthénium. Dans un deuxième temps, nous avons mis en place un dépôt électrochimique d'or avec un modèle dynamique à bulles d'hydrogène. Cette technique permet la fabrication d'une structure d'or en trois dimensions par le biais d'un dépôt d'or réalisé simultanément avec une évolution d'hydrogène. L'électrodéposition de l'oxyde de ruthénium sur cette structure poreuse a montré une très bonne compatibilité notamment en terme d'homogénéité du dépôt, une forte capacité à faible vitesse de balayage (> 3 F/cm2) et une bonne cyclabilité. Pour tester les performances de ces électrodes, nous avons réalisé un dispositif complet en configuration empilée présentant de bonnes caractéristiques. Cette technologie de fabrication a pu par ailleurs être transférée à la micro-échelle pour des dispositifs planaires à l'aide de procédés de photolithographie sur électrodes interdigitées. / The increasing importance of portable and wearable electronics as well as embedded wireless sensor networks has made energy autonomy a critical issue. Micro-energy autonomy solutions based on the combination of energy harvesting and storage may play a decisive role. However, the short lifetime of micro-batteries is problematic. Micro-supercapacitors are a promising solution in terms of energy storage for embedded systems on the account of their important lifetime. In this work we have focused on the optimization of the performances of micro-supercapacitors in terms of energy and power density. As the capacitance is directly related to the accessible surface area of the electrodes, we have investigated the structuration of the current collectors in order to improve the performances of ruthenium oxide-based micro-supercapacitors. Two mains technics have been studied to obtain three dimensional structures. In a first phase, the oblique angle physical vapor deposition (OAD) has been investigated to fabricate a columnar gold structure, subsequently covered by an electrochemical ruthenium oxide. In a second phase, a highly porous gold architecture has been studied using electrodeposition via a hydrogen bubbles dynamic template. The ruthenium oxide electrodeposited on the resulting mesoporous gold structure shows good compatibility, in terms of homogeneous deposition, with a significant capacitance at slow rate (> 3F.cm-2) and an important cyclability. As proof of concept, a device has been designed in a stack configuration with good performances. Moreover, the technology finalized for electrodes fabrication has been transferred to the micro-scale on planar interdigitated devices using a suitable photolithography process.