This paper addresses the challenge of generating optimal vehicle flow at the macroscopic level. Although several studies have focused on optimizing vehicle flow, little attention has been given to ensuring it can be practically achieved. To overcome this issue, we propose a route-recovery and eco-driving strategy for connected and automated vehicles (CAVs) that guarantees optimal flow generation. Our approach involves identifying the optimal vehicle flow that minimizes total travel time, given the constant travel demands in urban areas. We then develop a heuristic route-recovery algorithm to assign routes to CAVs that satisfy all travel demands while maintaining the optimal flow. Our method lets CAVs arrive at each road segment at their desired arrival time based on their assigned route and desired flow. In addition, we present an efficient coordination framework to minimize the energy consumption of CAVs and prevent collisions while crossing intersections. The proposed method can effectively generate optimal vehicle flow and potentially reduce travel time and energy consumption in urban areas.

翻译：本文从宏观层面解决了最优车流生成问题。尽管已有诸多研究聚焦于优化车流，但鲜有研究关注如何确保其实际可行性。针对这一不足，我们提出了一种面向网联自动驾驶车辆（CAVs）的路径恢复与生态驾驶策略，可保证最优车流的生成。该方法首先在市区恒定出行需求条件下，确定使总出行时间最小化的最优车流；继而开发启发式路径恢复算法，为CAVs分配满足所有出行需求且维持最优车流的行驶路径。该算法可使CAVs依据指定路径与期望车流，在预定时间点到达各路段。此外，我们提出了一种高效协同框架，在交叉口通行过程中最小化CAVs能耗并避免碰撞。所提方法能有效生成最优车流，有望降低市区出行时间与能耗。