TY - JOUR
T1 - Bi-level Programming Model for Resource-Shared Parking Lots Allocation
AU - Duan, Manzhen
AU - Wu, Dayong
AU - Liu, Hongchao
N1 - Funding Information:
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]. 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).
Funding Information:
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).
Publisher Copyright:
© 2019 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Intelligent transportation system
KW - bi-level design
KW - parking lots allocation model
KW - particle swarm optimization algorithm
KW - personalized parking guidance information system
UR - http://www.scopus.com/inward/record.url?scp=85089659807&partnerID=8YFLogxK
U2 - 10.1080/19427867.2019.1631596
DO - 10.1080/19427867.2019.1631596
M3 - Article
VL - 12
SP - 501
EP - 511
JO - Transportation Letters
JF - Transportation Letters
IS - 7
ER -