A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale
Parking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is cri...
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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/10.1155/2020/5624586 |
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| _version_ | 1832553261279739904 |
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| author | Ziyao Zhao Yi Zhang Yi Zhang |
| author_facet | Ziyao Zhao Yi Zhang Yi Zhang |
| author_sort | Ziyao Zhao |
| collection | DOAJ |
| description | Parking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is critical to the coordination management of multiple parks (e.g., district-scale or city-scale), is relatively limited. This study aims to analyse the performance of different prediction methods with regard to parking occupancy, considering parking type and parking scale. Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. To compare the predictive performances of these models, real-world data of four parks in Shenzhen, Shanghai, and Dongguan were collected over 8 weeks to estimate the correlation between the parking lot attributes and forecast results. As per the case studies, among the four models considered, SVM offers stable and accurate prediction performance for almost all types and scales of parking lots. For commercial, mixed functional, and large-scale parking lots, FM1 with SVM made the best prediction. For office and medium-scale parking lots, FM2 with SVM made the best prediction. |
| format | Article |
| id | doaj-art-645b0a1e830947829d831667d660caff |
| 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-645b0a1e830947829d831667d660caff2025-02-03T05:54:27ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/56245865624586A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking ScaleZiyao Zhao0Yi Zhang1Yi Zhang2Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, ChinaParking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is critical to the coordination management of multiple parks (e.g., district-scale or city-scale), is relatively limited. This study aims to analyse the performance of different prediction methods with regard to parking occupancy, considering parking type and parking scale. Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. To compare the predictive performances of these models, real-world data of four parks in Shenzhen, Shanghai, and Dongguan were collected over 8 weeks to estimate the correlation between the parking lot attributes and forecast results. As per the case studies, among the four models considered, SVM offers stable and accurate prediction performance for almost all types and scales of parking lots. For commercial, mixed functional, and large-scale parking lots, FM1 with SVM made the best prediction. For office and medium-scale parking lots, FM2 with SVM made the best prediction.http://dx.doi.org/10.1155/2020/5624586 |
| spellingShingle | Ziyao Zhao Yi Zhang Yi Zhang A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale Journal of Advanced Transportation |
| title | A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale |
| title_full | A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale |
| title_fullStr | A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale |
| title_full_unstemmed | A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale |
| title_short | A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale |
| title_sort | comparative study of parking occupancy prediction methods considering parking type and parking scale |
| url | http://dx.doi.org/10.1155/2020/5624586 |
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