A reservation and allocation model for shared-parking addressing the uncertainty in drivers’ arrival/departure time

Various solutions have been proposed to alleviate the shortage of parking places, including parking reservation systems and shared-parking systems. In such systems, drivers submit their parking requests in advance, especially their arrival and departure time. Then, the systems will reserve a proper parking spot for a driver if his/her parking request is accepted. However, the driver may arrive earlier or depart later, which may cause service failure. In shared-parking systems, the distributions of commuters’ arrival/departure time have fixed patterns and may be learned based on historical data. Given the distributions of drivers’ arrival/departure time, this paper proposes a Chance-constraint optimization model to solve the reservation and allocation problem for the shared-parking platform. This model aims to maximize the parking utilization level (i.e., the expectation of total occupied parking hours) and keep the service failure rate below a threshold value. The authors propose a rule-based mixed-integer linear programming to seek a satisfying solution to this model. Numerical tests show that the authors' model performs better than baseline models in indicators such as parking utilization level and service failure rate.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103484
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21004708
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Shuofeng
  • 0000-0002-4135-7941
  • Li, Zhiheng
  • Xie, Na
  • 0000-0001-7576-6391
• Publication Date: 2022-2

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 103484
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 135
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Departure time; Drivers; Reservations; Shared parking; Uncertainty
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01833099
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 21 2022 11:43AM