Residential Parking Service Model Based on Resource Sharing

• Published in: 东华大学学报（英文版） Vol. 32; no. 5; pp. 743 - 748
• Main Author: 段满珍 杨兆升 张林
• Format: Journal Article
• Language: English
• Published: School of Traffic, Jilin University, Changchun 130022, China%School of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan 063009, China 31.10.2015
• Subjects: Gamma distribution; parking sharing; urban parking; shifted negative exponential distribution (SNED); statistical analysis; residential parking
• ISSN: 1672-5220

Aiming at solving pressing parking issues in the urban environment, a residential parking spaces sharing model was proposed in this study. In this model, firstly, the residential community pattern, the status of idle parking spaces, and the temporal and spatial characteristics of sharing parking had been analyzed. Next,in the convenience of modeling,medical institutions that have the most prominent parking problems were selected as the subject of study. Based on the K-S statistical analysis results and the actual parking sharing situation,it was observed that the residential parking sharing time satisfied the shifted negative exponential distribution（ SNED）. Finally,a probability model of shared service capacity based on the SNED and critical time condition was established. By applying the statistical analysis method,the time of vehicles passing in and out of parking spaces, the idle time of parking spaces, the shifted distribution parameters, and other important model parameters had been calibrated,which was leading to the algorithm of model. In addition,considering the feasibility of model without sufficient data,the vehicle travel probability,the stagnation rate of parking space,and the status of parking spaces were defined and the reference data were also provided. The results of case studies indicate that it is very promising to solve urban parking issues if the residential community shares its rich parking resources with adjacent commercial buildings.