传统交通管理主要侧重于基于收费和道路定价的解决方案。然而，道路定价常引发关于可达性和公众不满的担忧，导致其在某些地区（如芬兰）被禁止。本研究优化了对驾驶员的动态激励分配，鼓励其改道至替代路径（可能更长），以实现更大的社会效益、降低交通网络中的总出行时间（TTT）和总排放量，从而促进可持续城市交通。我们采用多智能体强化学习方法动态分配激励，以减少 TTT 和排放，其中出行时间通过交通仿真软件估算。结果表明，在预算无限且以最小化出行时间为目标的情况下，与相当于 UE 总时间约 11% 预算的动态用户均衡（UE）相比，该激励方案使 TTT 降低了 16%。当以最小化排放为目标时，在无限预算下观察到 CO2 排放减少了 9%。我们展示了一个关键的权衡关系：最小化 TTT 会导致排放增加，而优先减少排放则会增加 TTT。然而，通过多目标函数中权重的适当组合，TTT 和总排放量均优于基线水平。

Traffic management has traditionally focused on toll-based and road pricing solutions. However, road pricing often raises concerns about accessibility and public dissatisfaction, leading to its prohibition in some places, such as Finland. This study optimises the dynamic allocation of incentives to drivers, encouraging them to reroute onto alternative (potentially longer) paths to achieve greater societal benefit, reduced total travel time (TTT) and total emissions in the transportation network, contributing to sustainable urban mobility. We employ a multi-agent reinforcement learning approach to dynamically assign incentives to drivers to reduce both TTT and emissions, with travel times estimated using traffic simulation software. We demonstrate that, with an unlimited budget and an objective of minimising travel time, the incentive scheme reduces TTT by 16%, compared to the dynamic User-Equilibrium (UE) with a budget equivalent to about 11% of the UE total time. When the goal is to minimise emissions, a 9% reduction in CO2 emissions is observed under an unlimited budget. We demonstrate a critical trade-off: minimising TTT leads to an increase in emissions, while prioritising emission reductions raises TTT. However, with the right combination of weights in the multi-objective function, both TTT and total emissions are improved beyond the baseline.