Routing strategies under the aegis of dynamic traffic assignment have been proposed in the literature to optimize system performance. However, challenges have persisted in their deployment ability and effectiveness due to inherent strong assumptions on traveler behavior and availability of network-level real-time traffic information, and the high computational burden associated with computing network-wide flows in real-time. To address these gaps, this study proposes an incentive-based decentralized routing strategy to nudge the network performance closer to the system optimum for the context where all vehicles are connected and autonomous vehicles (CAVs). The strategy consists of three stages. The first stage incorporates a local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles’ knowledge of local traffic information. This system is decentralized in the sense that it only updates the local route choices of vehicles in this area rather than route choices of all vehicles in the network, which circumvents the high computational burden associated with computing the flows on the entire network. The second stage optimizes the route for each CAV by considering individual heterogeneity in traveler preferences (e.g., the value of time) to maximize the utilities of all travelers in the local area. Constraints are also incorporated to ensure that these routes can achieve the approximated local system optimal flow of the first stage. The third stage leverages an expected envy-free incentive mechanism to ensure that travelers in the local area can accept the optimal routes determined in the second stage. The study analytically discusses the convergence of the local route switching dynamical system. We also show that the proposed incentive mechanism is expected individual rational and budget-balanced, which ensure that travelers are willing to participate and guarantee the balance between payments and compensations, respectively. Further, the conditions for the expected incentive compatibility of the incentive mechanism are analyzed and proved, ensuring behavioral honesty in disclosing information. Thereby, the proposed incentive-based decentralized routing strategy can enhance network performance and user satisfaction under fully connected and autonomous environments.

在动态流量分配的支持下，路由策略已经被提出来优化系统性能。然而，由于对旅行者行为和网络级实时交通信息的可用性的固有强力假设，以及与实时计算网络范围流量相关的高计算负担，其部署能力和有效性一直面临挑战。为了解决这些差距，本研究提出了一种基于激励的分散式路由策略，以使网络性能在所有车辆均已连接且无人驾驶汽车（CAV）连接的情况下更加接近系统最佳状态。该策略包括三个阶段。第一阶段结合了本地路线切换动力系统，以基于车辆对本地交通信息的了解来估计系统在本地区域中的最佳路线流量。该系统是分散的，因为它仅更新该区域内车辆的本地路线选择，而不更新网络中所有车辆的路线选择，从而避免了与计算整个网络上的流量相关的高计算负担。第二阶段通过考虑旅行者偏好中的个体异质性（例如时间值）来优化每个CAV的路线，以最大化本地所有旅行者的效用。还合并了约束条件，以确保这些路径可以实现第一阶段的近似本地系统最佳流量。第三阶段利用预期的无嫉妒激励机制来确保本地旅行者可以接受第二阶段确定的最佳路线。该研究分析性地讨论了本地路由交换动力学系统的收敛性。我们还表明，拟议的激励机制是期望个人理性和预算平衡的，这确保了旅行者愿意参与并保证分别支付和补偿之间的平衡。此外，对激励机制的预期激励相容性的条件进行了分析和证明，确保了信息披露中的行为诚实。因此，提出的基于激励的分散式路由策略可以在完全连接和自治的环境下提高网络性能和用户满意度。