Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm
With the increasingly significant trend of developing urban land for mixed-use, an increasing number of urban commercial and office complexes have been built. The parking demand characteristics of such buildings are more complex than the parking demand characteristics of single-use buildings due to...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2529912 |
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| _version_ | 1849306869028356096 |
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| author | Xiang Tang Jianxiao Ma Shun Zhou Tianci Shan |
| author_facet | Xiang Tang Jianxiao Ma Shun Zhou Tianci Shan |
| author_sort | Xiang Tang |
| collection | DOAJ |
| description | With the increasingly significant trend of developing urban land for mixed-use, an increasing number of urban commercial and office complexes have been built. The parking demand characteristics of such buildings are more complex than the parking demand characteristics of single-use buildings due to more diverse influencing factors. As there are complicated linear and nonlinear relationships between parking demand and influencing factors, it is difficult to accurately predict parking demand using a single multiple regression analysis (MRA) model. Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. In this paper, a two-level and ten-category index system is established and then mixed with the BP algorithm through the MRA model to improve the overall robustness and accuracy of the algorithm. Then, the BAS algorithm is used to search for optimal parameters involved in the BP neural network to avoid local optimization and improve the accuracy and efficiency of prediction. Finally, an instance analysis is carried out for verification, and the result indicates that the parking demand prediction accuracy of the MRA-BAS-BP algorithm is higher than the prediction accuracy of the traditional algorithm. |
| format | Article |
| id | doaj-art-20e68b1585394f7d93929396701ac6f3 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-20e68b1585394f7d93929396701ac6f32025-08-20T03:54:57ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2529912Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP AlgorithmXiang Tang0Jianxiao Ma1Shun Zhou2Tianci Shan3College of Automobile and Traffic EngineeringCollege of Automobile and Traffic EngineeringNanjing Institute of City & Transport Planning Co., Ltd.Nanjing Institute of City & Transport Planning Co., Ltd.With the increasingly significant trend of developing urban land for mixed-use, an increasing number of urban commercial and office complexes have been built. The parking demand characteristics of such buildings are more complex than the parking demand characteristics of single-use buildings due to more diverse influencing factors. As there are complicated linear and nonlinear relationships between parking demand and influencing factors, it is difficult to accurately predict parking demand using a single multiple regression analysis (MRA) model. Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. In this paper, a two-level and ten-category index system is established and then mixed with the BP algorithm through the MRA model to improve the overall robustness and accuracy of the algorithm. Then, the BAS algorithm is used to search for optimal parameters involved in the BP neural network to avoid local optimization and improve the accuracy and efficiency of prediction. Finally, an instance analysis is carried out for verification, and the result indicates that the parking demand prediction accuracy of the MRA-BAS-BP algorithm is higher than the prediction accuracy of the traditional algorithm.http://dx.doi.org/10.1155/2022/2529912 |
| spellingShingle | Xiang Tang Jianxiao Ma Shun Zhou Tianci Shan Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm Journal of Advanced Transportation |
| title | Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm |
| title_full | Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm |
| title_fullStr | Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm |
| title_full_unstemmed | Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm |
| title_short | Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm |
| title_sort | parking demand prediction method of urban commercial office complex buildings based on the mra bas bp algorithm |
| url | http://dx.doi.org/10.1155/2022/2529912 |
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