Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 54, Issue -, Pages 187-197Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2015.03.014
Keywords
Neural networks; Long short-term neural network; Traffic speed prediction; Remote microwave detector data
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Funding
- National Natural Science Foundation of China [51408019, 51308021, 51329801]
- Beijing Nova Programme
- Fundamental Research Funds for the Central Universities
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Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability. (C) 2015 Elsevier Ltd. All rights reserved.
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