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.

本文提出了一种基于卷积神经网络的方法，将交通数据通过二维时间空间矩阵转化成图像进行处理，并预测大规模网络流量，实验结果表明该方法在处理大规模交通网络数据方面表现出较高的准确性。