Managing parking with progressive pricing

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.