Who benefits from expanding transport networks? While designed to improve mobility, such interventions can also create inequality. In this paper, we show that disparities arise not only from the structure of the network itself but also from differences in how commuters adapt to it. We model commuters as reinforcement learning agents who adapt their travel choices at different learning rates, reflecting unequal access to resources and information. To capture potential efficiency-fairness tradeoffs, we introduce the Price of Learning (PoL), a measure of inefficiency during learning. We analyze both a stylized network -- inspired in the well-known Braess's paradox, yet with two-source nodes -- and an abstraction of a real-world metro system (Amsterdam). Our simulations show that network expansions can simultaneously increase efficiency and amplify inequality, especially when faster learners disproportionately benefit from new routes before others adapt. These results highlight that transport policies must account not only for equilibrium outcomes but also for the heterogeneous ways commuters adapt, since both shape the balance between efficiency and fairness.

翻译：交通网络扩建使谁受益？尽管此类干预措施旨在提升通行效率，但它们也可能催生不平等。本文证明，差异不仅源于网络结构本身，还源于通勤者适应方式的差异。我们将通勤者建模为强化学习智能体，它们以不同的学习速率调整出行选择，这反映了资源和信息获取的不平等。为捕捉效率与公平间的潜在权衡，我们引入了学习代价（PoL）——衡量学习过程中效率损失的指标。我们分析了一个受经典布雷斯悖论启发但具有双源节点的典型网络，以及阿姆斯特丹现实地铁系统的抽象模型。仿真结果表明，网络扩建可能同时提升效率并加剧不平等，尤其是当快速学习者能在他人适应前不成比例地获益于新路线时。这些发现强调，交通政策不仅需要考虑均衡结果，还必须考量通勤者适应方式的异质性，因为二者共同决定了效率与公平之间的平衡。