A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts
Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owin...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8898848 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849684983374938112 |
|---|---|
| author | Yunlin Guan Yun Wang Xuedong Yan Haonan Guo Yu Zhou |
| author_facet | Yunlin Guan Yun Wang Xuedong Yan Haonan Guo Yu Zhou |
| author_sort | Yunlin Guan |
| collection | DOAJ |
| description | Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China. |
| format | Article |
| id | doaj-art-bc48c4f9be454c0bbdfa219eda24f5b1 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-bc48c4f9be454c0bbdfa219eda24f5b12025-08-20T03:23:18ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88988488898848A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central DistrictsYunlin Guan0Yun Wang1Xuedong Yan2Haonan Guo3Yu Zhou4MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaMOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaMOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaMOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Civil and Environmental Engineering, National University of Singapore, 117576, SingaporeParking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China.http://dx.doi.org/10.1155/2020/8898848 |
| spellingShingle | Yunlin Guan Yun Wang Xuedong Yan Haonan Guo Yu Zhou A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts Journal of Advanced Transportation |
| title | A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts |
| title_full | A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts |
| title_fullStr | A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts |
| title_full_unstemmed | A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts |
| title_short | A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts |
| title_sort | big data driven framework for parking demand estimation in urban central districts |
| url | http://dx.doi.org/10.1155/2020/8898848 |
| work_keys_str_mv | AT yunlinguan abigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT yunwang abigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT xuedongyan abigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT haonanguo abigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT yuzhou abigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT yunlinguan bigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT yunwang bigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT xuedongyan bigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT haonanguo bigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts AT yuzhou bigdatadrivenframeworkforparkingdemandestimationinurbancentraldistricts |