This dissertation addresses the topic of sustainable transportation in the context of traffic signalized networks by optimizing both vehicle and traffic signal operations. From the vehicle side, the research develops a Green Light Optimal Speed Advisory (GLOSA) system also known as an Eco-Cooperative Adaptive Cruise Control system at intersections (ECO-CACC-I) for fixed and actuated traffic signals, using probabilistic traffic signal switching times to minimize vehicle fuel consumption through a computationally efficient A* minimum path algorithm within a dynamic programming procedure. This system explicitly minimizes vehicle fuel consumption while ensuring vehicle safety by preventing red light violations, hard braking, and excessive jerking. A sensitivity analysis is performed to quantify the impact of uncertainty in traffic signal timing predictions on fuel consumption. The research also extends the ECO-CACC-I system to integrate real-time back-of-the-queue estimation using loop detector and probe vehicle data with trajectory optimization, considering uncertainties in actuated traffic signal timings. The system enhances queue length estimates without relying on historical data and significantly improves upon the sole use of shockwave theory for the estimation of queues. On the infrastructure side, a cycle-free dynamic Decentralized Nash Bargaining (DNB) traffic signal controller is developed to optimize traffic signal operations using traffic stream density predictions, a flexible National Electrical Manufacturers Association (NEMA) phasing scheme, and dynamically adaptable control time steps. The DNB controller is benchmarked against fixed-time, actuated, and reinforcement (RL) machine learning (ML) control methods demonstrating its superior performance and simple algorithmic formulation. Furthermore, a two-stage Kalman filter algorithm is developed to predict traffic states for real-time traffic signal control, with the first stage estimating turning movement counts and the second stage estimating queue sizes and traffic stream density on the intersection approaches. This Kalman filter approach is integrated within the DNB controller to predict and optimize traffic signal timings in real time. The development of these vehicle and infrastructure systems aims to reduce vehicle energy consumption and emissions while improving traffic mobility. The proposed ECO-CACC-I system achieves average fuel savings of 37% and 30% for deterministic and stochastic settings respectively, compared to uninformed drivers. Furthermore, the ECO-CACC-I system demonstrates fuel savings of up to 18.89% when considering queue effects. The DNB traffic signal controller reduces average vehicle delay and queue sizes by up to 54% and 63%, respectively, compared to the state-of-the-practice Webster's pre-timed control. Finally, the analysis of the joint DNB-KF system showed benchmarks for the market penetration levels of connected vehicles that are required to achieve significant system performance. / Doctor of Philosophy / This dissertation addresses the topic of sustainability in transportation systems, where efficient systems for traffic lights and driver-assist systems are introduced. The problem is approached from two sides; 1) from the vehicle side, which represents efficient driver assist systems at several automation levels, and 2) from the infrastructure side, where traffic lights are optimized to accommodate approaching traffic efficiently and in an environmentally friendly manner. These systems aim to improve overall mobility performance by reducing the excessive wait time experienced at urban intersections at peak hours due to traffic congestion, and also reducing vehicle fuel consumption and harmful emissions. From the vehicle side, this research develops a system known as an Eco-Cooperative Adaptive Cruise Control system at intersections (ECO-CACC-I), which provides recommended speeds for drivers and for self-driving cars to minimize their fuel consumption, as well as reduces the delay at traffic lights. This system is designed to work at fixed-time signals, as well as actuated signals, which are widely common in the U.S. Different scenarios have been studied and analyzed in this dissertation to evaluate the system's performance. The research also extends the ECO-CACC-I system to consider surrounding vehicles, so that the system reduces collisions with other vehicles and avoids running red lights, which are a safety hazard. On the infrastructure side, this dissertation provides an enhanced and more reliable version of a traffic light controller, known as a DNB controller. This system provides a more flexible and adaptive traffic control sequence and green durations. The system is compared with currently common traffic light control systems such as fixed-time and actuated control strategies. In addition, this dissertation also develops a Kalman filtering system that aims to estimate and predict traffic measures, required for traffic analysis and traffic signal control. This system is based on data from connected vehicles and stationary sensors. This Kalman filter approach is integrated within the DNB controller to predict and optimize traffic signal timings in real time. The development of these vehicle and infrastructure systems aims to reduce vehicle energy consumption and emissions while improving vehicle mobility. The proposed ECO-CACC-I system achieves significant fuel savings and delay reductions, compared to the base case of uninformed drivers. Finally, the DNB traffic signal controller also results in significant vehicle delay and queue size reductions, compared to commonly used traffic light control methods.

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.