The time and frustration associated with finding a parking space during peak hours not only upsets drivers, but also significantly decreases the city’s economic, environmental, and social sustainability. Due to these issues, management of urban parking, via pricing schemes for example, is often necessary. A dynamic, demand based real-time pricing model was developed to optimally allocate parking spaces in busy urban centers, thus reducing congestion and other negative economic and environmental costs. This pricing model is online in nature and is able to react to real-time demand variations. It allows a parking agency to set system optimal pricing policies (e.g. to minimize congestion, maximize social surplus, maximize revenue) while considering user competition and market equilibrium.
The model was applied to a test case based off the Marina neighborhood in San Francisco. The network had 20 parking areas including 282 parking spaces on 19 on-street block faces. In this example, there were three main travel destinations and two travel origins, one at each of the two major access points. The test case was run over the entire enforceable day (9:00am – 6:00pm) with parking prices updated every 15 minutes. The demand data over time was estimated from TAZ-level Origin-Destination estimates provided by San Francisco County Transportation Authority (2009) from their activity-based travel demand model SF-CHAMP.
Four different parking management scenarios were modeled.
- Traditional scenario where drivers have no information at all
- Static Information scenario where drivers check parking availability and pricing information only when they start their trips
- Dynamic Information scenario where drivers are informed with updated parking space availability while driving
- Dynamic Pricing scenario where drivers can check updated near real-time availability information and prices are allowed to change at every time interval.
- The Dynamic Pricing model was found to be the most capable of achieving the ideal parking allocation (an occupancy level very close to the target level throughout the system at all times despite very high or very low demand), as indicated by the high amount of lot-hours that are not empty and yet still below the occupancy target.
- The model for the Dynamic Information scenario reduced circling (or excess driving distance) by 70 percent, 68 percent, and 56 percent for low, medium, and high demand levels, respectively (compared to the Traditional model).
- The model for the Dynamic Information scenario suggested a decrease of lost customers by 13 percent and 26 percent for medium and high demand levels, respectively (compared to the Traditional model).