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.

翻译：本研究提出了一种优化高速公路混合车流（包含人类驾驶车辆、不同载客量的自主车辆）的收费车道框架。自主车辆凭借更短跟车间距的能力可提升交通吞吐量。该框架指定一条收费车道供高载客自主车辆免费使用，其他车辆需缴纳费用。我们研究了所有车辆最小化出行成本时的车道选择均衡，并依据“机动性提升潜力”（本文定义为“机动度”）对车辆进行排序来刻画该均衡特性。通过数值算例展示了该框架在解决收费定价优化、载客阈值确定及车道政策设计等挑战中的应用价值，表明其促进了高载客车辆与自主车辆的协同整合。我们同时提出了一种通过合理定价降低通勤总延误的算法，并分析了违规逃费的影响。研究表明，自利驱动行为可缓解中等程度的违规影响，凸显了该框架的鲁棒性。本文首次对强调自主车辆与高载客车辆共存的收费车道框架进行了系统性分析，为交通管理优化及自主车辆融入现有基础设施提供了理论支撑。