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Conference Papers Year : 2017

Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway

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Di Zang
  • Function : Author
  • PersonId : 1033367
Jiawei Ling
  • Function : Author
Jiujun Cheng
  • Function : Author
Keshuang Tang
  • Function : Author
Xin Li
  • Function : Author
  • PersonId : 758835
  • IdRef : 193139367

Abstract

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan’an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them.
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Dates and versions

hal-01820901 , version 1 (22-06-2018)

Licence

Attribution - CC BY 4.0

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Di Zang, Jiawei Ling, Jiujun Cheng, Keshuang Tang, Xin Li. Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.212-221, ⟨10.1007/978-3-319-68121-4_22⟩. ⟨hal-01820901⟩
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