Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events
This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
ELSEVIER
2023
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/LP170100341 http://hdl.handle.net/20.500.11937/96005 |
| _version_ | 1848766070741008384 |
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| author | Aljuaydi, Fahad Wiwatanapataphee, Benchawan Wu, Yong |
| author_facet | Aljuaydi, Fahad Wiwatanapataphee, Benchawan Wu, Yong |
| author_sort | Aljuaydi, Fahad |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks have been developed to predict traffic flow under a road crash and the rain. Using an input dataset with five features (the flow rate, the speed, and the density, road incident and rainfall) and two standard metrics (the Root Mean Square error and the Mean Absolute error), models’ performance is evaluated. |
| first_indexed | 2025-11-14T11:45:18Z |
| format | Journal Article |
| id | curtin-20.500.11937-96005 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:45:18Z |
| publishDate | 2023 |
| publisher | ELSEVIER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-960052024-10-15T06:27:05Z Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events Aljuaydi, Fahad Wiwatanapataphee, Benchawan Wu, Yong Science & Technology Technology Engineering, Multidisciplinary Engineering Non-recurrent events Traffic prediction Multivariate model Machine Learning LSTM This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks have been developed to predict traffic flow under a road crash and the rain. Using an input dataset with five features (the flow rate, the speed, and the density, road incident and rainfall) and two standard metrics (the Root Mean Square error and the Mean Absolute error), models’ performance is evaluated. 2023 Journal Article http://hdl.handle.net/20.500.11937/96005 10.1016/j.aej.2022.10.015 English http://purl.org/au-research/grants/arc/LP170100341 http://creativecommons.org/licenses/by/4.0/ ELSEVIER fulltext |
| spellingShingle | Science & Technology Technology Engineering, Multidisciplinary Engineering Non-recurrent events Traffic prediction Multivariate model Machine Learning LSTM Aljuaydi, Fahad Wiwatanapataphee, Benchawan Wu, Yong Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| title | Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| title_full | Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| title_fullStr | Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| title_full_unstemmed | Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| title_short | Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| title_sort | multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events |
| topic | Science & Technology Technology Engineering, Multidisciplinary Engineering Non-recurrent events Traffic prediction Multivariate model Machine Learning LSTM |
| url | http://purl.org/au-research/grants/arc/LP170100341 http://hdl.handle.net/20.500.11937/96005 |