Across the world, increasing numbers of cars in urban centers lead to congestion and adverse effects on public health as well as municipal climate goals. Reflecting cities’ ambitions to mitigate these issues, a growing body of research evaluates the use of innovative pricing strategies for parking, such as Dynamic Pricing (DP), to efficiently manage parking supply and demand. We contribute to this research by exploring the effects of Reinforcement Learning (RL)-based DP on urban parking. In particular, we introduce a theoretical framework for AI-based priced parking under traffic and social constraints. Furthermore, we present a portable and generalizable Agent-Based Model (ABM) for priced parking to evaluate our approach and run extensive experiments comparing the effect of several learners on different urban policy goals. We find that (1) outcomes are highly sensitive to the employed reward function; (2) trade-offs between different goals are challenging to balance; (3) single-reward functions may have unintended consequences on other policy areas (e.g., optimizing occupancy punishes low-income individuals disproportionately). In summary, our observations stress that fair DP schemes need to account for social policy measures beyond traffic parameters such as occupancy or traffic flow.
