Modeling and Managing the Parking Sharing Problem for Urban Cities

This study models the parking sharing problem in urban cities, where private parking owners can share their vacant spaces to parking users via an e-platform, and then examines the platform operator’s pricing strategies for revenue-maximization or social-cost-minimization. The model takes into account the spatial dimension of parking that both public curbside spaces and private ones potentially available for sharing are distributed along the travel corridor between the city center and the residential area. On the supply side, private parking owners can rent or “sell the right-of use” of their spaces to the platform based on the rent they can receive and the inconvenience they would suffer by sharing. On the demand side, travelers make their parking choices of space type (curbside or shared) and location (distance from the city center) under given parking capacities and prices. The resulting parking choice equilibrium is formulated as a minimization problem and the underlying properties of the equilibrium are identified and discussed. Based on the supply-demand equilibrium, the pricing strategies (rent paid to space owners and price charged on space users) of the platform operator are investigated. Numerical examples are presented to illustrate the models and results, and also to provide further insights.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Liu, Wei
    • Zhang, Fangni
    • Wang, Xiaolei
    • Shao, Chaoyi
    • Yang, Hai
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01698244
  • Record Type: Publication
  • Report/Paper Numbers: 19-02646
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 1 2019 3:51PM