Bi-level Programming Model for Resource-Shared Parking Lots Allocation

Manzhen Duan, Dayong Wu, Hongchao Liu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The paper proposed a Personalize Parking Guidance Service(PPGS), in which a bi-level programming model was built to describe the relationship between the Personalized Parking Guidance Information System and drivers. The upper-level of the model aimed to achieve the efficetive and balanced utilization of parking resonurces during peak time, while the lower-level model was to minimize  the driver's walking distance after parking.A nested Particel Swarm Optimization algorithm was used to solve the proposed model. The simulation results of the model show that the peak congestion time has been reduced remarkably under the guidance of the proposed model. The Mean value of Unocuupied Parking Difference Index(MUPDI) curves trend to decline during the overall process. It means that within the acceptable walking distance, the proposed parking lots allocation model can effectively balance the utilization of parking resources shared in the service area and minimize walking distance as well.

Original languageEnglish
Pages (from-to)501-511
Number of pages11
JournalTransportation Letters
Issue number7
StatePublished - Jun 2019


  • Intelligent transportation system
  • bi-level design
  • parking lots allocation model
  • particle swarm optimization algorithm
  • personalized parking guidance information system


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