Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/10/2229 |
| _version_ | 1798024230823002112 |
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| author | Sen Zhang Yong Yao Jie Hu Yong Zhao Shaobo Li Jianjun Hu |
| author_facet | Sen Zhang Yong Yao Jie Hu Yong Zhao Shaobo Li Jianjun Hu |
| author_sort | Sen Zhang |
| collection | DOAJ |
| description | Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency. |
| first_indexed | 2024-04-11T17:59:03Z |
| format | Article |
| id | doaj.art-c3a828b03df641a994463123321010a8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T17:59:03Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c3a828b03df641a994463123321010a82022-12-22T04:10:34ZengMDPI AGSensors1424-82202019-05-011910222910.3390/s19102229s19102229Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation NetworksSen Zhang0Yong Yao1Jie Hu2Yong Zhao3Shaobo Li4Jianjun Hu5Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550003, ChinaCollege of Big Data Statistics, GuiZhou University of Finance and Economics, Guiyang 550025, ChinaDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USASchool of Mechanical Engineering, Guizhou University, Guiyang 550003, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550003, ChinaTraffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.https://www.mdpi.com/1424-8220/19/10/2229transportation networktraffic congestion forecastingspatial-temporal correlationdeep learningend-to-enddeep autoencoderconvolutional neural networklong short-term memory |
| spellingShingle | Sen Zhang Yong Yao Jie Hu Yong Zhao Shaobo Li Jianjun Hu Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks Sensors transportation network traffic congestion forecasting spatial-temporal correlation deep learning end-to-end deep autoencoder convolutional neural network long short-term memory |
| title | Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks |
| title_full | Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks |
| title_fullStr | Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks |
| title_full_unstemmed | Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks |
| title_short | Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks |
| title_sort | deep autoencoder neural networks for short term traffic congestion prediction of transportation networks |
| topic | transportation network traffic congestion forecasting spatial-temporal correlation deep learning end-to-end deep autoencoder convolutional neural network long short-term memory |
| url | https://www.mdpi.com/1424-8220/19/10/2229 |
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