Bi-level programming model for resource-shared parking lots allocation

ABSTRACT

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.

KEYWORDS:

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

Acknowledgments

This research has been jointly supported by the National Natural Science Fund Project (Grant No.51378171) and North China University of Science and Technology PhD Research Start-up Fund Project (Grant No.BS2017054).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the North China University of Science and Technology PhD Research Start-up Fund Project [BS2017054]; China National Natural Science Fund Project [CNSF 51378171].