Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 69, Issue 1, Pages 101-113Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2952605
Keywords
Traffic flow prediction; ensemble learning; long short term memory neural network; no negative constraint theory; population extremal optimization
Categories
Funding
- National Natural Science Foundation of China [61872153, 61972288]
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Accurate and stable short-term traffic flow prediction is an indispensable part in current intelligent transportation systems. In this paper, a novel short-term traffic flow forecasting model termed as EnLSTM-WPEO is proposed based on ensemble learning of long short term memory neural network (LSTM), no negative constraint theory (NNCT) weight integration and population extremal optimization (PEO) algorithm. In the first stage, a cluster of LSTMs is constructed to separately forecast with different time lag, which is a significant element to affect the prediction performance. In the second stage, the PEO-based NNCT weight integration strategy is introduced to determine the weight coefficients of the ensemble model. The simulation results for six different datasets from highways of Seattle have testified the superiority of the proposed EnLSTM-WPEO to other six popular traffic flow forecasting models in terms of two commonly used performance indices and three statistical tests.
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