A parallel spatiotemporal deep learning network for highway traffic flow forecasting
Spatiotemporal features have a significant influence on traffic flow prediction. Due to the potentially internal relationship of adjacent roads, spatial information can, to some extent, affect traffic flow forecasting. Simultaneously, periodic information of traffic flow data can also be positively...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719832792 |
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| Summary: | Spatiotemporal features have a significant influence on traffic flow prediction. Due to the potentially internal relationship of adjacent roads, spatial information can, to some extent, affect traffic flow forecasting. Simultaneously, periodic information of traffic flow data can also be positively affected by temporal features. Considering these key points, this article proposes a parallel spatiotemporal deep learning network for short-term highway traffic flow forecasting, which learns features from the time and space dimensions. In the introduced model, the convolutional neural network is used to extract spatial features and long short-term memory is used to extract temporal features of traffic flow. The parallel-connected structure of convolutional neural network and long short-term memory reflects much powerful performance in traffic flow prediction. To apply the parallel spatiotemporal deep learning network in large dataset prediction, a dataset of Shanghai inner ring elevated road is used to predict 591 sensors in 6 months. Experimental results confirm that the overall performance of our parallel spatiotemporal deep learning network surpasses those of other state-of-the-art methods. |
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| ISSN: | 1550-1477 |