A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting

Abstract

Accurate and timely traffic flow forecasting is essential for many intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Over the past two decades, a variety of traffic flow forecasting models have been proposed. While each model has its merits and can achieve satisfactory forecasting results under certain traffic conditions, it is difficult for a single model to deal with various conditions well. In this paper, we proposed a novel deep learning-based multimodel integration framework in order to overcome the limitations of previous methods in dealing with large variations and uncertainties of traffic flow and hence improve the forecasting accuracy. Our framework can dynamically choose an optimal model or an optimal subset of models from a set of candidate models to forecast the future traffic flow conditions according to current input data. We employ stacked autoencoder (SAE), a simple yet efficient deep learning architecture, to extract the implicit relationships hidden in the traffic flow data and employed labeled data to fine tune the parameters of the architecture. Compared with the hand-crafted features and explicable dependence relations leveraged in previous models, the features learning from SAE are more representative and hence have more powerful forecasting capability. In addition, we propose a model-driven scheme to automatically label the training data and develop three strategies to integrate multiple models. Extensive experiments performed on three typical traffic flow datasets demonstrate the proposed framework outperforms state-of-the-art models and achieves much more accurate forecasting results under large and sudden variations.