A tripartite-coordinated dynamic pricing model for urban off-street parking facilities considering parking choice behavior and period- specific parking demand price elasticity

Urban off-street parking often suffers from spatiotemporal supply–demand imbalances. This study proposes a dynamic pricing method integrating drivers' parking choice behaviour and time-varying demand price elasticity. A parking choice model combines driver preferences estimated from stated preference surveys with operational conditions derived from revealed preference observations. Period-specific elasticities are incorporated as demand-response feedback into a coordinated framework involving regulators, parking operators, and drivers. The resulting non-convex problem is solved using a simulated annealing-enhanced genetic algorithm. A case study in Shapingba District, Chongqing, reveals substantial temporal variation in price sensitivity, with demand elasticity reaching −1.044 during 14:00–17:00. Compared with static pricing, the proposed strategy reduces the peak–trough occupancy disparity by 39.53%, increases operator revenue by 11.12%, and lowers average driver travel cost by 14.74%. These findings demonstrate its effectiveness in improving resource allocation, profitability, and consumer welfare.