Is CAV participatory traffic control socially beneficial? Incentive mechanisms and policy design

The rise of connected and automated vehicles (CAVs) presents new opportunities for traffic management through participatory traffic control, where government agencies recruit CAVs to act as mobile actuators, adjusting their travel choices and driving maneuvers to enhance overall system efficiency. While existing studies focus primarily on control algorithms, a key challenge remains unaddressed: how to effectively incentivize privately owned CAVs to participate, which often incurs both travel cost burdens and psychological resistance. To provide high-level insights to this question, this paper develops a stylized morning commute model to evaluate the system-level benefits of recruiting CAV users under varying levels of control relinquishment. We formulate a social-welfare optimization problem and characterize the first-best recruitment and incentive design. General structural results establish when participatory control can be socially beneficial, while analytical results under simplifying assumptions yield additional insights into how optimal recruitment is allocated across user groups. These insights are complemented by numerical experiments under alternative scenarios. We also study a revenue-neutral extension in which congestion tolls finance incentive payments, and show that this scheme can achieve Pareto improvements. Finally, we calibrate the framework to the San Francisco–Oakland Bay Bridge to illustrate the practical magnitude of the mechanism. Overall, the paper provides a benchmark theoretical framework for incentive design in participatory CAV-based traffic control.

Duke Scholars

Author Jerome P. Lynch Civil and Environmental Engineering