This paper develops incentive mechanisms for promoting eco-driving with the overarching goal of minimizing emissions in transportation networks. The system operator provides drivers with energy-efficient driving guidance throughout their trips and measures compliance through vehicle telematics that capture how closely drivers follow this guidance. Drivers optimize their behaviors based on personal trade-offs between travel times and emissions. To design effective incentives, the operator elicits driver preferences regarding trip urgency and willingness to eco-drive, while determining optimal budget allocations and eco-driving recommendations. Two distinct settings based on driver behavior are analyzed. When drivers report their preferences truthfully, an incentive mechanism ensuring obedience (drivers find it optimal to follow recommendations) is designed by implementing eco-driving recommendations as a Nash equilibrium. When drivers may report strategically, the mechanism is extended to be both obedient and truthful (drivers find it optimal to report truthfully). Unlike existing works that focus on congestion or routing decisions in transportation networks, our framework explicitly targets emissions reduction by incentivizing drivers. The proposed mechanism addresses both strategic behavior and network effects arising from driver interactions, without requiring the operator to reveal system parameters to the drivers. Numerical simulations demonstrate the effects of budget constraints, driver types, and strategic misreporting on equilibrium outcomes and emissions reduction.

翻译：本文开发了旨在促进生态驾驶的激励机制，其总体目标是最大限度地减少交通网络中的排放。系统运营商在整个行程中为驾驶员提供节能驾驶指导，并通过车辆远程信息处理技术（捕捉驾驶员遵循指导的紧密程度）来衡量其依从性。驾驶员根据出行时间与排放之间的个人权衡来优化自身行为。为设计有效的激励机制，运营商需获取驾驶员关于行程紧迫性和生态驾驶意愿的偏好信息，同时确定最优预算分配方案和生态驾驶建议。本文分析了基于驾驶员行为的两种不同情境：当驾驶员如实报告其偏好时，通过将生态驾驶建议设计为纳什均衡，构建了确保服从性（驾驶员发现遵循建议是最优选择）的激励机制；当驾驶员可能策略性报告时，该机制被扩展为兼具服从性与真实性（驾驶员发现如实报告是最优选择）。与现有研究主要关注交通网络中的拥堵或路径决策不同，本框架通过激励驾驶员的方式，明确以减排为目标。所提出的机制同时解决了驾驶员交互产生的策略性行为和网络效应，且无需运营商向驾驶员公开系统参数。数值模拟展示了预算约束、驾驶员类型和策略性误报对均衡结果及减排效果的影响。