Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China
Abstract This study was focused on Hangzhou in China that are undergoing large‐scale subway construction, and an improved momentum back‐propagation (BP) neural network model was trained. The model can analyze the complex traffic data, evaluate the service quality of bus line, and improve the estimat...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-07-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12074 |
| _version_ | 1811266475701305344 |
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| author | Peiqing Li Shunfeng Zhang Biqiang Zhong Jin Wu Hao Zhang Yikai Chen Yang Fu Qibing Wang Qipeng Li |
| author_facet | Peiqing Li Shunfeng Zhang Biqiang Zhong Jin Wu Hao Zhang Yikai Chen Yang Fu Qibing Wang Qipeng Li |
| author_sort | Peiqing Li |
| collection | DOAJ |
| description | Abstract This study was focused on Hangzhou in China that are undergoing large‐scale subway construction, and an improved momentum back‐propagation (BP) neural network model was trained. The model can analyze the complex traffic data, evaluate the service quality of bus line, and improve the estimation accuracy and convergence speed. For the same training data set, the convergence time of the BP algorithm with momentum term is reduced by 0.043 secs, the iterative convergence speed is improved by 0.66%, and the estimation accuracy is improved by 26.7% compared with the standard BP algorithm. Under similar conditions, the convergence time is 1.562 secs less than that of the standard BP algorithm, and the convergence speed was 24.1% higher than that of the standard BP algorithm, and the absolute value of the estimated error was less than 1%. Finally, a representative bus line in Hangzhou was used as an example to evaluate the model. The results showed that the improved momentum BP neural network model had a faster convergence speed and higher prediction accuracy of the comprehensive weight of bus line service quality. The prediction results of the model are consistent with the actual survey results, which indicates that the model constructed is reasonable. |
| first_indexed | 2024-04-12T20:42:46Z |
| format | Article |
| id | doaj.art-94fc6d5a9430430bb5174bb96b102f19 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-12T20:42:46Z |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-94fc6d5a9430430bb5174bb96b102f192022-12-22T03:17:21ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-07-0115795897210.1049/itr2.12074Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in ChinaPeiqing Li0Shunfeng Zhang1Biqiang Zhong2Jin Wu3Hao Zhang4Yikai Chen5Yang Fu6Qibing Wang7Qipeng Li8School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou 310023 ChinaSchool of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou 310023 ChinaHangzhou Institute of Communications Planning Design and Research Hangzhou 310006 ChinaHangzhou Institute of Communications Planning Design and Research Hangzhou 310006 ChinaSchool of Transportation Southeast University Nanjing 211189 ChinaSchool of Automotive and Traffic Engineering Hefei University of Technology Hefei 230009 ChinaSchool of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou 310023 ChinaSchool of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou 310023 ChinaSchool of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou 310023 ChinaAbstract This study was focused on Hangzhou in China that are undergoing large‐scale subway construction, and an improved momentum back‐propagation (BP) neural network model was trained. The model can analyze the complex traffic data, evaluate the service quality of bus line, and improve the estimation accuracy and convergence speed. For the same training data set, the convergence time of the BP algorithm with momentum term is reduced by 0.043 secs, the iterative convergence speed is improved by 0.66%, and the estimation accuracy is improved by 26.7% compared with the standard BP algorithm. Under similar conditions, the convergence time is 1.562 secs less than that of the standard BP algorithm, and the convergence speed was 24.1% higher than that of the standard BP algorithm, and the absolute value of the estimated error was less than 1%. Finally, a representative bus line in Hangzhou was used as an example to evaluate the model. The results showed that the improved momentum BP neural network model had a faster convergence speed and higher prediction accuracy of the comprehensive weight of bus line service quality. The prediction results of the model are consistent with the actual survey results, which indicates that the model constructed is reasonable.https://doi.org/10.1049/itr2.12074AsiaInstrumentation and techniques for geophysical, hydrospheric and lower atmosphere researchOptimisation techniquesInterpolation and function approximation (numerical analysis)Traffic engineering computingNeural nets |
| spellingShingle | Peiqing Li Shunfeng Zhang Biqiang Zhong Jin Wu Hao Zhang Yikai Chen Yang Fu Qibing Wang Qipeng Li Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China IET Intelligent Transport Systems Asia Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optimisation techniques Interpolation and function approximation (numerical analysis) Traffic engineering computing Neural nets |
| title | Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China |
| title_full | Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China |
| title_fullStr | Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China |
| title_full_unstemmed | Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China |
| title_short | Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China |
| title_sort | service quality evaluation of bus lines based on improved momentum back propagation neural network model a study of hangzhou in china |
| topic | Asia Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optimisation techniques Interpolation and function approximation (numerical analysis) Traffic engineering computing Neural nets |
| url | https://doi.org/10.1049/itr2.12074 |
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