Parking supply and demand are often imbalanced in urban areas, causing adverse consequences such as excessive search times and long walking distances. Many parking authorities price parking as a demand management strategy by charging either a fixed daily fee or an hourly price for parking. An emerging alternative is progressive pricing, whereby drivers pay an hourly price that increases if their tracked parking duration is longer than a predetermined threshold. This study investigates the optimal design of progressive pricing for revenue and social welfare maximization when there are two market segments. We study equilibrium properties of progressive pricing and show that its optimal design for revenue maximization segmentizes the demand. In contrast, progressive pricing does not improve social welfare over hourly pricing, because any additional surplus accrued by drivers is offset by the revenue of the parking authority. We develop a micro-simulation model of the City of Toronto’s downtown core with parking capability, and show that progressive pricing lowers average parking occupancy and search time in high demand parking clusters by 5.6% and 12.5%, respectively, compared to hourly pricing.

城市地区的停车位供需往往失衡，造成搜索时间过长、步行距离过长等不良后果。许多停车管理机构通过收取固定的每日停车费或每小时停车费，将停车定价作为一种需求管理策略。一种新兴的替代方案是累进定价，如果司机跟踪的停车时间长于预定阈值，则司机按小时支付费用。本研究调查了存在两个细分市场时收入和社会福利最大化的累进定价的优化设计。我们研究了累进定价的均衡属性，并表明其收入最大化的优化设计对需求进行了细分。相比之下，累进定价并没有比按小时定价提高社会福利，因为司机积累的任何额外盈余都被停车管理局的收入抵消了。我们开发了具有停车能力的多伦多市市中心的微观模拟模型，并表明与按小时定价相比，累进定价将高需求停车集群的​​平均停车位占用率和搜索时间分别降低了 5.6% 和 12.5%。