traffic flow prediction model based on deep belief network and genetic algorithm
Traffic flow prediction plays an indispensable role in the intelligent transportation system. The effectiveness of traffic control and management relies heavily on the prediction accuracy. The authors propose a model based on deep belief networks (DBNs) to predict the traffic flow. Moreover, they use Fletcher–Reeves conjugate gradient algorithm to optimise the fine-tuning of model's parameters. Since the traffic flow has various features at different times such as weekday, weekend, daytime and night-time, the hyper-parameters of the model should adapt to the time. Therefore, they employ the genetic algorithm to find the optimal hyper-parameters of DBN models for different times. The dataset from Caltrans Performance Measurement System was used to evaluate the performance of their models. The experimental results demonstrate that the proposed model achieved better performance in different times.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
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Supplemental Notes:
- Abstract reprinted with permission of the Institution of Engineering and Technology.
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Authors:
- Zhang, Yaying
- Huang, Guan
- Publication Date: 2018-6
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 533-541
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Serial:
- IET Intelligent Transport Systems
- Volume: 12
- Issue Number: 6
- Publisher: Institution of Engineering and Technology (IET)
- ISSN: 1751-956X
- EISSN: 1751-9578
- Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Genetic algorithms; Intelligent transportation systems; Mathematical prediction; Traffic flow
- Uncontrolled Terms: Deep belief networks
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01676366
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 26 2018 2:38PM