Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach
Parking demand forecasting is an important part of urban parking planning and is also an important basis for the development of parking facilities. The primary objective of this study was to explore multiple factors that affect the curb parking price (CPP) and the changing rules of the curb parking...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/4905059 |
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| author | Yan Wan Jibiao Zhou Wenqiang He Changxi Ma |
| author_facet | Yan Wan Jibiao Zhou Wenqiang He Changxi Ma |
| author_sort | Yan Wan |
| collection | DOAJ |
| description | Parking demand forecasting is an important part of urban parking planning and is also an important basis for the development of parking facilities. The primary objective of this study was to explore multiple factors that affect the curb parking price (CPP) and the changing rules of the curb parking price (CPP) with these factors and to predict the CPP in terms of urban mobility. The data were collected through a statistical survey that was administered in 81 cities in China. The cities were divided into three categories: rich cities (RCs), poor cities (PCs), and tourist cities (TCs). Both the time series method (TSM) and regression analysis method (RAM) were developed to simultaneously examine the factors associated with the CPP among parking users. The results showed that TSM and RAM can account for common urban curb parking prices. The prediction results showed that the CPP is affected by the number of urban dwellers (UD), the prevalence of car ownership (CO), and the per capita disposable income (PCDI) of urban residents; the CPP can be predicted by a model built on the basis of the above three influencing factors. The results can enhance our understanding of the factors that affect CPP. Based on the results, some suggestions regarding the use of the CPP range in parking policy planning were discussed. |
| format | Article |
| id | doaj-art-6629fc173aec4e9384e22029e22f3977 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-6629fc173aec4e9384e22029e22f39772025-02-03T01:05:12ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/49050594905059Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM ApproachYan Wan0Jibiao Zhou1Wenqiang He2Changxi Ma3School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaDepartment of Transportation Engineering, Tongji University, Shanghai 201804, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, ChinaParking demand forecasting is an important part of urban parking planning and is also an important basis for the development of parking facilities. The primary objective of this study was to explore multiple factors that affect the curb parking price (CPP) and the changing rules of the curb parking price (CPP) with these factors and to predict the CPP in terms of urban mobility. The data were collected through a statistical survey that was administered in 81 cities in China. The cities were divided into three categories: rich cities (RCs), poor cities (PCs), and tourist cities (TCs). Both the time series method (TSM) and regression analysis method (RAM) were developed to simultaneously examine the factors associated with the CPP among parking users. The results showed that TSM and RAM can account for common urban curb parking prices. The prediction results showed that the CPP is affected by the number of urban dwellers (UD), the prevalence of car ownership (CO), and the per capita disposable income (PCDI) of urban residents; the CPP can be predicted by a model built on the basis of the above three influencing factors. The results can enhance our understanding of the factors that affect CPP. Based on the results, some suggestions regarding the use of the CPP range in parking policy planning were discussed.http://dx.doi.org/10.1155/2020/4905059 |
| spellingShingle | Yan Wan Jibiao Zhou Wenqiang He Changxi Ma Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach Journal of Advanced Transportation |
| title | Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach |
| title_full | Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach |
| title_fullStr | Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach |
| title_full_unstemmed | Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach |
| title_short | Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach |
| title_sort | modeling the curb parking price in urban center district of china using tsm ram approach |
| url | http://dx.doi.org/10.1155/2020/4905059 |
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