Optimal dynamic parking pricing for morning commute considering expected cruising time

• Published in: Transportation research. Part C, Emerging technologies Vol. 48; pp. 468 - 490
• Main Authors: Qian, Zhen (Sean), Rajagopal, Ram
• Format: Journal Article
• Language: English
• Published: Elsevier India Pvt Ltd 01.11.2014
• Subjects: Clusters; Demand; Dynamic tests; Dynamics; Morning; Optimal parking pricing; Optimization; Parking; Parking cruising time; Parking management; Parking occupancy; Pricing; Parking cruising time; Parking management; Optimal parking pricing; Parking occupancy
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2014.08.020

•Parking demand in the morning commute can be managed by dynamic pricing.•System optimal parking flow pattern and prices are formulated by a linear program.•Optimal terminal parking occupancy exists and is unique under our network settings.•Optimal parking flow can be achieved by setting location-based pricing schemes.•Optimal parking prices can be set dependent on the time or time-varying occupancy. This paper investigates how recurrent parking demand can be managed by dynamic parking pricing and information provision in the morning commute. Travelers are aware of time-varying pricing information and time-varying expected occupancy, through either their day-to-day experience or online information provision, to make their recurrent parking choices. We first formulate the parking choices under the User Equilibrium (UE) conditions using the Variational Inequality (VI) approach. More importantly, the System Optimal (SO) parking flow pattern and SO parking prices are also derived and solved efficiently using Linear Programming. Under SO, any two parking clusters cannot be used at the same time by travelers between more than one Origin–Destination (O–D) pairs. The SO parking flow pattern is not unique, which offers sufficient flexibility for operators to achieve different management objectives while keeping the flow pattern optimal. We show that any optimal flow pattern can be achieved by charging parking prices in each area that only depend on the time or occupancy, regardless of origins and destinations of users of this area. In the two numerical experiments, the best system performance is usually achieved by pricing the more preferred (convenient) area such that it is used up to a terminal occupancy of around 85–95%. Optimal pricing essentially balances the parking congestion (namely cruising time) and the level of convenience.