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

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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.

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Acknowledgements

This work was supported partially by the National Natural Science Foundation of China (Nos. 61472145, 61772206, U1611461), in part by Special Fund of Science and Technology Research and Development on Application From Guangdong Province(SF-STRDA-GD No. 2016B010124011), in part by Guangdong High-level personnel of special support program (No. 2016TQ03X319) and the Guangdong Natural Science Foundation (No. 2017A030311027, No. 2016A030313047). The authors would like to thank Dr. Yubin Wang, from SIM Industries, Sassenheim, Netherlands, who provides the careful collected and preprocessed traffic flow data from the motorways of Amsterdam.

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering, Shantou University, Shantou, China

    Teng Zhou

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Guoqiang Han & Xuemiao Xu

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong

    Chu Han

  4. College of Mathematics and Information, South China Agricultural University, Guangzhou, China

    Yuchang Huang

  5. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jing Qin

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  1. Teng Zhou
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Correspondence to Xuemiao Xu.

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Zhou, T., Han, G., Xu, X. et al. A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting. Neural Process Lett 49, 407–430 (2019). https://doi.org/10.1007/s11063-018-9804-x

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