Traffic Parameters Prediction Using a Three-Channel Convolutional Neural Network - Intelligence Science I (ICIS 2017) Access content directly
Conference Papers Year : 2017

Traffic Parameters Prediction Using a Three-Channel Convolutional Neural Network

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

Abstract

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation Systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance.
Fichier principal
Vignette du fichier
978-3-319-68121-4_39_Chapter.pdf (538.72 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01820914 , version 1 (22-06-2018)

Licence

Attribution

Identifiers

Cite

Di Zang, Dehai Wang, Jiujun Cheng, Keshuang Tang, Xin Li. Traffic Parameters Prediction Using a Three-Channel Convolutional Neural Network. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.363-371, ⟨10.1007/978-3-319-68121-4_39⟩. ⟨hal-01820914⟩
82 View
72 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More