Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach
The intelligent transportation system (ITS) has been proven capable of effectively addressing traffic congestion issues. For vehicles to perform effectively and improve mobility under the intelligent driving environment, real-time prediction of traffic speed is undoubtedly essential. Considering the...
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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/10.1155/2022/6888115 |
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| author | Chengying Hua Wei (David) Fan |
| author_facet | Chengying Hua Wei (David) Fan |
| author_sort | Chengying Hua |
| collection | DOAJ |
| description | The intelligent transportation system (ITS) has been proven capable of effectively addressing traffic congestion issues. For vehicles to perform effectively and improve mobility under the intelligent driving environment, real-time prediction of traffic speed is undoubtedly essential. Considering the complex spatiotemporal dependency inherent in traffic data, conventional prediction models encounter many limitations. To improve the prediction performance and investigate the temporal features, this study focuses on emerging deep neural networks (DNNs) using the Caltrans Performance Measurement System (PeMS) data. This research also establishes an intelligent driving environment in the simulation and compares the traditional car-following model with deep learning methods in terms of multiple performance metrics. The results indicate that both supervised learning and unsupervised learning are superior to the simulation-based model on the freeway, and the two deep learning networks are almost identical to one another. Besides, the result reveals that all models have their latent features for different time dimensions under the low traffic loads, transition states, and heavy traffic loads. This is critical in the application of prediction technologies in ITS. The findings can assist transportation researchers and traffic engineers in both traffic operation and management, such as bottleneck identification, platooning control, and route planning. |
| format | Article |
| id | doaj-art-0587f3d0d9374125a60e7e89ceba59eb |
| 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-0587f3d0d9374125a60e7e89ceba59eb2025-02-03T01:24:36ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6888115Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning ApproachChengying Hua0Wei (David) Fan1USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE)The intelligent transportation system (ITS) has been proven capable of effectively addressing traffic congestion issues. For vehicles to perform effectively and improve mobility under the intelligent driving environment, real-time prediction of traffic speed is undoubtedly essential. Considering the complex spatiotemporal dependency inherent in traffic data, conventional prediction models encounter many limitations. To improve the prediction performance and investigate the temporal features, this study focuses on emerging deep neural networks (DNNs) using the Caltrans Performance Measurement System (PeMS) data. This research also establishes an intelligent driving environment in the simulation and compares the traditional car-following model with deep learning methods in terms of multiple performance metrics. The results indicate that both supervised learning and unsupervised learning are superior to the simulation-based model on the freeway, and the two deep learning networks are almost identical to one another. Besides, the result reveals that all models have their latent features for different time dimensions under the low traffic loads, transition states, and heavy traffic loads. This is critical in the application of prediction technologies in ITS. The findings can assist transportation researchers and traffic engineers in both traffic operation and management, such as bottleneck identification, platooning control, and route planning.http://dx.doi.org/10.1155/2022/6888115 |
| spellingShingle | Chengying Hua Wei (David) Fan Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach Journal of Advanced Transportation |
| title | Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach |
| title_full | Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach |
| title_fullStr | Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach |
| title_full_unstemmed | Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach |
| title_short | Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach |
| title_sort | freeway traffic speed prediction under the intelligent driving environment a deep learning approach |
| url | http://dx.doi.org/10.1155/2022/6888115 |
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