EnLSTM-WPEO: Short-Term Traffic Flow Prediction by Ensemble LSTM, NNCT Weight Integration, and Population Extremal Optimization

Abstract

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.