Policies and Strategies for Evolving and Managing Automated Mobility

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The deployment of automated vehicles (AVs) is rapidly approaching with a push from governments who are relaxing laws to allow AVs to operate on highways, and industry, both manufacturers and mobility service providers, who are heavily investing in the development of the technology and its applications. AVs are expected to tremendously enhance the efficiency, safety and convenience of existing transportation systems. However, all these benefits hinge on the level of market penetration of AVs being sufficiently high. At low market shares, AVs exert little impact on enhancing transportation system efficiency. Worse yet, early deployment of AVs may even compromise the efficiency. The transition period is expected to be lengthy. If we can shorten it, the tremendous benefits promised by AVs can be realized sooner. This grant thus sets out to investigate incentivizing policies and innovative traffic management strategies to promote the development and deployment of AVs to maximize the social benefit over the entire duration of the AV deployment. Specifically, incentivizing policies will nurture the AV market and accelerate their adoption while innovative traffic management schemes aim to better utilize AVs in the traffic stream and promote high-occupancy mobility services to maximize the benefits of AVs at a given market share. The synergies between incentivizing policies and traffic management schemes may create an upward spiral for the AV deployment and particularly reduce the duration of initial deployment where AVs exert little or even negative impact on enhancing efficiency. This grant will provide timely support for government agencies to better understand the impacts and implications of AVs and provides guidance on their development and deployment. This grant will involve students at all levels and traditionally underrepresented students, and offer fresh materials and case studies for courses on emerging automated mobility. Research results will be broadly disseminated through a variety of media.The research will be conducted in two main thrusts. The first is the study of policies like tax credits, subsidies, and preferential treatments for AVs etc., which can incentivize the deployment of AVs from lower to higher penetration rates to maximize the benefits of AV deployment throughout a planning horizon. As part of the first thrust, we plan a continuous time principal-agent framework in which the government is the principal who offers an incentive policy, and the manufacturer is the agent who sets the retail prices of AVs. The optimal incentive mechanism is obtained by considering the interplay between these two entities. In the second thrust, we will develop innovative schemes to improve the social welfare of the transportation system. These schemes could involve headway-based congestion pricing for penalizing excessive headways of prototype AVs or occupancy-based pricing for promoting high occupancy mobility. In parallel, a distributed control scheme will be developed to use AVs in the traffic stream as control actuators to distribute traffic demand across the transportation network to reduce congestion. As the impacts of incentivizing policies from Thrust 1 and traffic management strategies from Thrust 2 are intertwined, an iterative application of the models developed in both thrusts can prescribe a wise course of actions to evolve and manage automated mobility. If successful, this grant makes three critical contributions: a continuous time principal-agent approach for incentive policy analysis, data-driven headway- or occupancy-based congestion pricing and distributed control of AVs for managing traffic flow. Specifically, we formulate the incentivizing policy design as a non-zero dynamic Stackelberg game under asymmetric information, a class of problems extremely difficult to solve using traditional techniques. We offer an innovative framework to decouple the decision-making processes to make the problem mathematically tractable. Our research also advances the theory of congestion pricing by providing a new framework of designing fine-grained pricing schemes based on vehicle trajectory and occupancy. It shifts the paradigm from model-based pricing to be more data-driven. The distributed control of AVs for managing network traffic flow enriches the traffic control literature and theorizes participatory traffic control that is distributed, scalable and effective.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.