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Sensors 2017, 17(4), 818; doi:10.3390/s17040818

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

1
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China
2
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
3
School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
*
Author to whom correspondence should be addressed.
Received: 30 January 2017 / Revised: 18 March 2017 / Accepted: 7 April 2017 / Published: 10 April 2017
(This article belongs to the Special Issue Sensors for Transportation)
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Abstract

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. View Full-Text
Keywords: transportation network; traffic speed prediction; spatiotemporal feature; deep learning; convolutional neural network transportation network; traffic speed prediction; spatiotemporal feature; deep learning; convolutional neural network
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Ma, X.; Dai, Z.; He, Z.; Ma, J.; Wang, Y.; Wang, Y. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors 2017, 17, 818.

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