Parking Generating Rate Prediction Method Based on Grey Correlation Analysis and SSA-GRNN

Chao Zeng (), Xu Zhou, Li Yu and Changxi Ma
Additional contact information
Chao Zeng: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Xu Zhou: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Li Yu: School of Geography and Planning, Cardiff University, Cardiff CF10 3WT, UK
Changxi Ma: College of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Sustainability, 2023, vol. 15, issue 17, 1-15

Abstract: The parking generating rate model is commonly used in parking demand forecasting. However, the key indicators of the parking generating rate are generally difficult to determine, especially its future annual value. The parking generating rate is affected by many factors. In order to more accurately predict the urban parking generating rate, this paper establishes a parking generating rate prediction model based on grey correlation analysis and a generalized regression neural network (GRNN) optimized by a sparrow search algorithm (SSA). Gross domestic product (GDP), urban area, urban population, motor vehicle ownership, and land use type are selected as input variables of the GRNN via grey correlation analysis. The SSA is used to optimize network weights and thresholds, and a model based on the SSA to optimize the GRNN is constructed to predict the parking generating rate of different cities. The results show that, after SSA optimization, the maximum absolute error of the GRNN model in predicting the parking generating rate is reduced, and the prediction accuracy of the model is effectively improved. This model can provide technical support for solving urban parking problems.

Keywords: prediction of parking generating rate; grey relational analysis; sparrow search algorithm; generalized regression neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/17/13016/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/17/13016/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:17:p:13016-:d:1228193

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().