UC Berkeley

With a growing number of vehicles and an increasing complexity of transportation systems, traffic management and traffic optimization become more and more crucial in mitigating congestion, reducing travel delay and improving traffic state. The concept of a “Smart” city that incorporates various intelligent systems related to infrastructure modification, wireless communication, networking and centralized/decentralized controllers is considered to be the next evolutionary stage of the modern urban world. This dissertation focuses on optimization models and algorithms leveraging traffic management apps (navigation and reservation) and vehicle-to-everything (V2X) communication for mitigating congestion level, reducing fuel consumption and minimizing travel delay for vehicles in urban areas. An ability to share data, receive and send requests and directions allows traffic agents, both on-road (moving) and off-road (parked), to significantly improve utilization of transportation resources. To demonstrate the impact network-level control policies have on a system’s social delay, in our work [1], presented in Chapter 2, we propose a greedy optimization algorithm that eliminates “Braess” routes and derives a paradox-free subnetwork to be implemented in navigation apps. Prior literature that studied the Braess paradox was not able to provide efficient tools for improving the equilibrium state. Topology analysis methods could only predict the occurrence of the paradox but could not deal with it. Results that focused on a single link or route removal were ineffective for large networks. Other methods that discussed tolling or closing roads were too restrictive and required significant infrastructure modifications. Our approach, on the other hand, is more flexible and can be effectively applied to real-world systems to completely eliminate the inefficiency and momentarily reduce total travel time. In addition, we address the challenging task of incorporating queue delay into the network representation by introducing “phantom links”. The following chapters focus on link-level models dealing with local traffic inefficiencies. In Chapters 3 (corresponding work [2]) and 4 (extension to the work [3]) we explore moving traffic management methods and benefits their implementation has with respect to traffic throughput, travel time and fuel consumption. We demonstrate how the optimal platoon formation algorithm can improve traffic progression on urban streets and freeways. Earlier methods either focused on the Ad-hoc protocols, which have limited application in mixed traffic due to its short range, or tried to reduce the travel delay at the cost of the increased traffic disturbance, which was both ineffective and potentially harmful. Our approach, aimed at travel time minimization, takes on the local clustering method and proposes an intelligent platoon merging system that addresses a major part of its common complications and difficulties. In particular, minor infrastructure modifications accompanied by V2I communication protocols enable efficient localization and coordination with minimal traffic disruption and make it possible to artificially increase road capacity and improve congested regions. For fuel consumption, we discuss a novel queue estimation procedure, built upon the vehicle labeling system presented in [3], to be used in a prediction-based model that ties together Speed Advisory System and actuated traffic lights to reduce idling at intersections and smooth driving patterns. Moving on from thru-traffic optimization, in Chapter 5 we focus on curb management issues and propose a delivery vehicles’ operation hours partitioning model to be implemented in a reservation app. Previous works that discussed parking reservations did not consider delivery vehicles and their specific arrival patterns and focused mostly on general cars. Another commonly used parking policy, dynamic parking pricing, extensively studied in the past, is not effective when dealing with delivery vehicles, since they do not usually pay for parking. Our system, on the other hand, focuses on implementing parking equilibrium by physically constraining delivery trucks’ parking choices. In particular, this model is aimed at eliminating double-parking. In Chapter 6 we start looking at practical implementation of curb management techniques. More specifically, we investigate a new delivery vehicle activity monitoring method via dashcam video footage and data analysis algorithms. Switching from the non-scalable and costly data collection techniques, such as static surveillance, we propose a more flexible, low-cost and effective model for parking patterns recognition and curb management implementation.