Research On External Shared Parking In Residential Areas

With the increasing number of motor vehicles in China,the contradiction between the demand of urban parking and the supply of berths has become more and more prominent,especially in the city center business circle,the shortage of parking resources is extremely serious.Through the analysis of the usage rules of the allocated berths of various types of buildings in the city,it can be found that there is an obvious time complementarity between the parking Spaces of residential areas and those of other buildings.The parking peak time of residential areas is mostly in the night,and the utilization rate is low in the daytime,that is,residential berths have the possibility of external sharing in the daytime.In order to make full use of the huge berth resources contained in the residential area,part of the berths in the residential area can be shared out in specific periods on the premise of satisfying the normal use of the owners,which can not only improve the utilization rate of the berths,but also alleviate the current situation of urban parking difficulties to a certain extent.Firstly,this thesis analyzes the feasibility of external shared parking in residential areas from five aspects: policy,theory,technology,users and owners,and puts forward four measures to ensure the smooth implementation of berth sharing,which provides a theoretical basis for the external sharing of residential areas.The framework of the shared parking management platform is proposed,and the functions of the platform are discussed in detail.Secondly,the thesis analyzes the arrival and departure characteristics and spare berth characteristics of motor vehicles in residential areas,and finds that there are completely different parking rules between working days and rest days.On the basis of summarizing parking rules,the time series model,BP neural network and GA-optimized BP neural network are established respectively to predict the number of free berths.The historical free berth data was used for prediction verification analysis.By comparing the time series prediction method with the prediction errors of BP neural network before and after optimization,it was found that the prediction accuracy of the optimized BP neural network was higher.Then,based on the prediction results of free berths,the division method of berths sharing time window is proposed.By constraining the opening time and number of shared berths,reserved berths are set to ensure the normal use of berths by residential owners to the maximum extent.Finally,the rationality of the prediction model of free berth and the method of dividing the time window of shared berth is verified by an example.