title: Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.
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.
authors.affiliation: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. xiaolei@buaa.edu.cn.
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Ma
authors.affiliation: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. zhuangdai@buaa.edu.cn.
authors.forename: Zhuang
authors.initials: Z
authors.lastname: Dai
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. he.zb@hotmail.com.
authors.forename: Zhengbing
authors.initials: Z
authors.lastname: He
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. jhma@bjtu.edu.cn.
authors.forename: Jihui
authors.initials: J
authors.lastname: Ma
authors.affiliation: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China. yongwx6@gmail.com.
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. ypwang@buaa.edu.cn.
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Wang
keywords: convolutional neural network; deep learning; spatiotemporal feature; traffic speed prediction; transportation network