Abstract
Despite rapid advances of information technologies for intelligent parking systems, it remains a challenge to optimally manage limited parking resources in busy urban neighborhoods. In this paper, we use dynamic location-dependent parking pricing and reservation to improve system-wide performance of an intelligent parking system. With this system, the parking agency is able to decide the spatial and temporal distribution of parking prices to achieve a variety of objectives, while drivers with different origins and destinations compete for limited parking spaces via online reservation. We develop a multi-period non-cooperative bi-level model to capture the complex interactions among the parking agency and multiple drivers, as well as a non-myopic approximate dynamic programming (ADP) approach to solve the model. It is shown with numerical examples that the ADP-based pricing policy consistently outperforms alternative policies in achieving greater performance of the parking system, and shows reliability in handling the spatial and temporal variations in parking demand.
Original language | English (US) |
---|---|
Pages (from-to) | 226-244 |
Number of pages | 19 |
Journal | Transportation Research Part C: Emerging Technologies |
Volume | 77 |
DOIs | |
State | Published - Apr 1 2017 |
Keywords
- Approximate dynamic programming
- Dynamic pricing
- Equilibrium
- MPEC
- Parking management
ASJC Scopus subject areas
- Transportation
- Automotive Engineering
- Civil and Structural Engineering
- Management Science and Operations Research