NCAE and ELM Based Enhanced Ensemble Optimized Model for Traffic Flow Forecasting

Accurate and timely traffic flow forecasting is the key technology to address the issue of urban traffic congestion, which is significant for intelligent transportation system. However, it is a quite challenging task to develop an efficient, robust and automatically generated forecasting model. In this paper, we propose a novel model, ensemble forecasting architecture based on non-negative constrained sparse autoencoder and extreme learning machine(NCAE-ELM-EA). This model is designed using NCAE to extract traffic characteristics layer-by-layer through a greedy unsupervised algorithm, and the prediction network formed by connecting NCAE and ELM serves as base learner of each lag pool. Then, employing adaptive enhanced integration algorithm to build an ensemble optimized forecasting model with scalable size suitable for each road segment. The model was applied to the actual data collected from I5 NB highways in Portland, USA, and J6-J7(N) freeways in the United Kingdom, with higher accuracy and lower labor costs compared with existing predictors.