Research On Intelligent Parking Model Based On Big Data Analysis

In recent years,with the rapid development of the domestic economy,people’s quality of life has been continuously improved,and the family’s demand for cars has also been increasing.In the process of urban development,the problem of parking difficulties has become more and more serious.In the management of urban parking lots,due to the lack of analysis of parking lot data by the government and other relevant departments,urban parking lots are prone to congestion and vacancy.In response to this situation,this article has carried out research on smart parking models based on big data environments.The model research mainly analyzes and studies parking data and parking lots in cities,which provides a data basis for government decision-making analysis.The main research content is divided into the following two points:1.For the study of parking data,this paper constructs a TMLR-ARMA model based on a combination of multiple linear regression models and autoregressive moving average models.The model introduces time,weather,temperature and other influencing factors.Forecast the number of parking in the parking lot in the future.During the research process,comparative experiments with multiple linear regression models,differential integrated moving average autoregressive models,and seasonal exponential shift models were designed to verify the accuracy and effectiveness of the smart parking model.Finally,a quick solution matching mode is proposed.The matching mode compares the prediction data with the decision-making data in government parking lot management.Combined with user evaluation data,it can provide solutions for parking lots that need to be rectified.Quick match,so as to help the government in the parking lot management decision-making,provide effective solutions and data basis.2.The research on parking lots is mainly based on the current government’s increasingly regionalized management of parking lots.Aiming at the effective division of public parking areas in cities,this paper proposes a clustering analysis method based on feature weighting.In the cluster analysis,canopy clustering analysis and K-means clustering analysis methods are used,combined with the parking lot usage model and geographic location,a 5-dimensional feature vector is constructed,and the cluster analysis based on feature weighting is performed on the urban parking lot.The clustering results are verified by the TMLR-ARMA model and the value of₈),and the effectiveness of this method in the planning of urban parking areas is determined,thereby helping the government to provide effective information in the decision-making of urban parking areas.The help and reliable data basis.