Journal
SENSORS
Volume 17, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/s17040818
Keywords
transportation network; traffic speed prediction; spatiotemporal feature; deep learning; convolutional neural network
Funding
- National Natural Science Foundation of China [51408019, 71501009, U1564212]
- Beijing Nova Program [z151100000315048]
- Beijing Natural Science Foundation [9172011]
- Young Elite Scientist Sponsorship Program by the China Association for Science and Technology
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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.
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