Journal
APPLIED SOFT COMPUTING
Volume 78, Issue -, Pages 712-721Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2018.09.040
Keywords
Traffic flow prediction; Deep learning; Intelligent transportation systems
Categories
Funding
- Fundamental Research Funds for the Central Universities [106112017CDJXY090002, CDJXS10182216]
- Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201800120]
- Subproject VI of National Key Research and Development Program Project [2018YFF0214706, 2018YFF0214700]
- National Natural Science Foundations of China [61672119, 61379158, 61672117]
- National Program on Key Basic Research Project (973 Program) [2013CB328903]
- Natural Science Foundation of Chongqing [cstc2017jcyjBX0025]
- Major Program on Science and Technology of Guanxi Province of China [GKAA17129002]
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Traffic flow prediction is a fundamental component in intelligent transportation systems. Various computational methods have been applied in this field, among which machine learning based methods are believed to be promising and scalable for big data. In general, most of machine learning based methods encounter three fundamental issues: feature representation of traffic patterns, learning from single location or network, and data quality. In order to address these three issues, in this work we present a deep architecture for traffic flow prediction that learns deep hierarchical feature representation with spatio-temporal relations over the traffic network. Furthermore, we design an ensemble learning strategy via random subspace learning to make the model be able to tolerate incomplete data. Accordingly the contributions of this work are summarized as the three points. First, we transform the time series analysis problem into the task of image-like analysis. Benefitting from the image-like data form, we can jointly explore spatio-temporal relations simultaneously by the two-dimension convolution operator. In addition, the proposed model can tolerate the incomplete data, which is very common in traffic application field. Finally, we propose an improved random search based on uniform design in order to optimize hyper-parameters for deep Convolutional Neural Networks (deep CNN). A large range of experiments with various traffic conditions have been performed on the traffic data originated from the California Freeway Performance Measurement System (PeMS). The experimental results corroborate the effectiveness of the proposed approach compared with the state of the art. (C) 2018 Elsevier B.V. All rights reserved.
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