Enhancing Traffic Efficiency of Mixed Traffic Using Control-based and Learning-based Connected and Autonomous Systems

Inefficient traffic operations have far-reaching consequences in not just travel mobility but also public health, social equity, and economic prosperity. Congestion, a key symptom of inefficient operations, can lead to increased emissions, accidents, and productivity loss. Therefore, advancements and policies in transportation engineering require careful scrutiny to not only prevent unintended adverse consequences but also capitalize on opportunities for improvement. In spite of significant efforts to enhance traffic mobility and safety, human control of vehicles remains prone to errors such as inattention, impatience, and intoxication. Connected and autonomous vehicles (CAVs) are seen as a great opportunity to address human-related inefficiencies. This dissertation focuses on connectivity and automation and investigates the synergies between technologies. First, a deep reinforcement learning based strategy is proposed herein to enable agents to address the dynamic nature of inputs in traffic environments and to capture proximal and distant information, and to facilitate learning in rapidly changing traffic. The strategy is applied to alleviate congestion at highway bottlenecks by training a small number of CAVs to cooperatively reduce congestion through deep reinforcement learning. Secondly, to address congestion at intersections, the dissertation introduces a fog-based graphic RL (FG-RL) framework. This approach allows traffic signals across multiple intersections to form a cooperative coalition, sharing information for signal timing prescriptions. Large-scale traffic signal optimization is computationally inefficient, so the proposed FG-RL approach breaks down networks into smaller fog nodes that function as local centralization points within a decentralized system Doing so allows for a bottom-up solution approach for decomposing large traffic networks. Furthermore, the dissertation pioneers the notion of using a small CAV fleet, selected from any existing shared autonomous mobility services (SAMSs) to assist city road agencies to achieve string-stable driving in locally congested urban traffic. These vehicles are dispersed throughout the network to perform their primary function of providing ride-share services. However, when they encounter severe congestion, they act cooperatively with each other to be rerouted and to undertake traffic-stabilizing maneuvers to smoothen the traffic and reduce congestion. The smoothing behavior is learned through DRL, while the rerouting is facilitated through the proposed constrained entropy-based dynamic AV routing algorithm (CEDAR).