  An emerging public health application of connected and automated vehicle
(CAV) technologies is to reduce response times of emergency medical service
(EMS) by indirectly coordinating traffic. Therefore, in this work we study the
CAV-assisted corridor clearance for EMS vehicles from a short term deployment
perspective. Existing research on this topic often overlooks the impact of EMS
vehicle disruptions on regular traffic, assumes 100% CAV penetration, relies on
real-time traffic signal timing data and queue lengths at intersections, and
makes various assumptions about traffic settings when deriving optimal
model-based CAV control strategies. However, these assumptions pose significant
challenges for near-term deployment and limit the real-world applicability of
such methods. To overcome these challenges and enhance real-world applicability
in near-term, we propose a model-free approach employing deep reinforcement
learning (DRL) for designing CAV control strategies, showing its reduced
overhead in designing and greater scalability and performance compared to
model-based methods. Our qualitative analysis highlights the complexities of
designing scalable EMS corridor clearance controllers for diverse traffic
settings in which DRL controller provides ease of design compared to the
model-based methods. In numerical evaluations, the model-free DRL controller
outperforms the model-based counterpart by improving traffic flow and even
improving EMS travel times in scenarios when a single CAV is present. Across 19
considered settings, the learned DRL controller excels by 25% in reducing the
travel time in six instances, achieving an average improvement of 9%. These
findings underscore the potential and promise of model-free DRL strategies in
advancing EMS response and traffic flow coordination, with a focus on practical
near-term deployment.
