Reference
Q. Tao, Z. Li, J. Xu, S. Lin, B. De Schutter, and
J. A. K. Suykens, "Short-term traffic
flow prediction based on the efficient hinging hyperplanes neural network,"
IEEE Transactions on Intelligent Transportation
Systems, vol. 23, no. 9, pp. 15616-15628, Sept. 2022.
Abstract
Traffic flow (TF) prediction is an important and yet a challenging task in
transportation systems, since the TF involves high nonlinearities and is
affected by many elements. Recently, neural networks have attracted much
attention for TF prediction, but they are commonly black boxes with complex
architectures and difficult to be interpreted, e.g., the contributions of
specific traffic elements are not explicit, hardly providing informative
guidance. In this paper, we aim at addressing more interpretable short-term TF
prediction with joint consideration to high accuracy, and thus introduces a
pragmatic method by applying the efficient hinging hyperplanes neural network
(EHHNN) simply built upon sparse neuron connections. In the proposed method,
different traffic factors are incorporated into the inputs, including their
spatial-temporal information. Besides the pursuit of accuracy, we further
extend the ANOVA decomposition of EHHNNs to the interpretation analysis with
specifications to traffic data, in which the contributions concerning specific
traffic variables are detected quantitatively. As such, the proposed method
firstly applies the EHHNN to filter out more important traffic variables for
dimensionality reduction while maintaining accurate prediction. Then, variable
interpretation analysis is performed from different perspectives, e.g. to
quantitatively investigate the influence of traffic factors and also their
spatial-temporal impacts. Therefore, a predictor and an analyzing tool can both
be attained for the TF by exerting the flexibility and extending the
interpretability of EHHNNs, which is promising to provide informative guidance
to future traffic control. Numerical experiments verify the effectiveness and
potential of the proposed method in TF prediction and analysis.
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BibTeX
@article{TaoLi:22-015,
author = {Tao, Qinghua and Li, Zhen and Xu, Jun and Lin, Shu and De
Schutter, Bart and Suykens, Johan A. K.},
title = {Short-Term Traffic Flow Prediction Based on the Efficient Hinging
Hyperplanes Neural Network},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {23},
number = {9},
pages = {15616--15628},
month = sep,
year = {2022}
}