An overall analysis method of urban road parking lots based on data mining Online publication date: Mon, 02-Aug-2021
by Guanlin Chen; Jiapeng Shen; Jiang He; Xu Dai; Wenyong Weng
International Journal of Security and Networks (IJSN), Vol. 16, No. 2, 2021
Abstract: In this paper, we first propose a multiple linear regression-autoregressive moving average model (MLR-ARMA) which combines the multiple linear regression model and the autoregressive moving average model to fit and predict a single parking lot's parking demand. The experimental results show that this model performs better on predicting future parking amounts than the simple multiple linear regression model and the autoregressive integrated moving average (ARIMA) model. Then, this paper proposes an overall analysis method of urban road parking lots based on cluster analysis and uses the MLR-ARMA model to verify the clustering results. The experimental results show that when reasonable weights are assigned to different dimensions of the feature vector of parking lots, the method proposed in this paper can classify parking lots with similar usage patterns and adjacent locations into one category well, which is conducive to further analysis.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Security and Networks (IJSN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook