Traffic congestion in cities necessitates proactive demand management instruments that balance efficiency and equity. Traditional tools like congestion taxes often face significant public resistance. In comparison, Tradable Mobility Credit (TMC) schemes offer a politically viable, revenue-neutral alternative. While the instrument’s effectiveness is proven in theory, actual travelers’ behavioral mechanisms observed in empirical studies are often ignored in analytical models for tractability, leading to biased estimations and difficulties in real-world implementation. Built on an interdisciplinary collaboration between the Technical University of Munich (TUM) and École Polytechnique Fédérale de Lausanne (EPFL), this project bridges the gap between theoretical economic efficiency and realistic behavioral mechanisms. The central goal is to design an efficient, equitable, and user-friendly Adaptive Tradable Mobility Credit (ATMC) scheme with enhanced practicalities. The research consists of four interconnected modules: (i) Empirical Perception quantifies behavioral inputs and civic utility through experiments; (ii) Analysis & Calibration integrates empirical findings into analytical models; (iii) Concept & Solution Design optimizes ATMC design to guarantee Pareto-improving system performance; and (iv) Testing and Benchmarking uses large-scale simulation to validate effectiveness and robustness of proposed system design and derived actionable policy. This comprehensive approach delivers a necessary blueprint for next-generation mobility regulation.