  In this study, we introduce a toll lane framework that optimizes the mixed
flow of autonomous and high-occupancy vehicles on freeways, where human-driven
and autonomous vehicles of varying commuter occupancy share a segment.
Autonomous vehicles, with their ability to maintain shorter headways, boost
traffic throughput. Our framework designates a toll lane for autonomous
vehicles with high occupancy to use free of charge, while others pay a toll. We
explore the lane choice equilibria when all vehicles minimize travel costs, and
characterize the equilibria by ranking vehicles by their mobility enhancement
potential, a concept we term the mobility degree. Through numerical examples,
we demonstrate the framework's utility in addressing design challenges such as
setting optimal tolls, determining occupancy thresholds, and designing lane
policies, showing how it facilitates the integration of high-occupancy and
autonomous vehicles. We also propose an algorithm for assigning rational tolls
to decrease total commuter delay and examine the effects of toll
non-compliance. Our findings suggest that self-interest-driven behavior
mitigates moderate non-compliance impacts, highlighting the framework's
resilience. This work presents a pioneering comprehensive analysis of a toll
lane framework that emphasizes the coexistence of autonomous and high-occupancy
vehicles, offering insights for traffic management improvements and the
integration of autonomous vehicles into existing transportation
infrastructures.
