@article{ref1,
title="Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction",
journal="Sensors (Basel)",
year="2017",
author="Ma, Xiaolei and Dai, Zhuang and He, Zhengbing and Ma, Jihui and Wang, Yong and Wang, Yunpeng",
volume="17",
number="4",
pages="s17040818-s17040818",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s17040818",
url="http://dx.doi.org/10.3390/s17040818"
}