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. Finally, we present an efficient coordination framework to minimize the energy consumption of CAVs while safely crossing intersections. The proposed method can effectively generate optimal vehicle flow and potentially reduce travel time and energy consumption in urban areas.

翻译：本文探讨了在宏观层面上生成最优车流面临的核心挑战。尽管已有诸多研究聚焦于车流优化，但鲜有关注如何确保其实际可行性。为突破这一瓶颈，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，旨在保障最优车流的生成。该方法首先在城域恒定出行需求条件下，识别出能最小化总出行时间的最优车流。继而设计了一种启发式路径恢复算法，为CAVs分配行驶路径。最后，构建了高效的协调框架，在保障交叉口安全通行的同时，最小化CAVs的能耗。所提方法能够有效生成最优车流，并有望降低城域出行时间与能源消耗。