This chapter proposes a dynamic pricing strategy for fast charging stations (FCSs) in coupled power-traffic networks, explicitly considering bounded user rationality and market regulation. Unlike traditional models that assume full rationality and static pricing, this work establishes a tri-level Stackelberg game framework. At the upper level, the distribution network operator determines locational marginal prices (LMPs) via an optimal power flow model. The middle level features an FCS operator who sets dynamic retail prices to maximize profit, subject to a novel regulatory constraint that limits the revenue coefficient to ensure user affordability. At the lower level, electric vehicle (EV) users exhibit bounded rationality, making spatiotemporal charging decisions based on a bounded rationality dynamic user equilibrium (BRDUE) model. To solve this complex hierarchical fixed-point problem, a Gauss–Seidel iterative algorithm with a damping factor is developed, ensuring computational efficiency and convergence. Furthermore, a data-driven adaptive method is introduced to calibrate user tolerance parameters from historical data, enhancing model practicality. Case studies on a coupled 33-bus power and 17-node traffic network demonstrate that the proposed optimization-based dynamic pricing strategy significantly improves FCS operator profits compared to fixed or LMP-based methods. The market regulation mechanism effectively controls user costs, preventing excessive charges. The strategy also successfully shifts charging loads to off-peak periods, reduces distribution network operating costs, and alleviates traffic congestion at charging stations. Sensitivity analysis reveals that moderate bounded rationality can enhance system efficiency by mitigating user overreaction, while the regulatory coefficient is a critical lever for balancing profitability and fairness. This research provides a robust, implementable framework for coordinated FCS pricing in modern coupled infrastructure systems.