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Driving Behaviour Style Study with a Hybrid Deep Learning Framework Based on GPS Data

Author

Listed:
  • Jingqiu Guo

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China)

  • Yangzexi Liu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China)

  • Lanfang Zhang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China)

  • Yibing Wang

    (Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China)

Abstract

Innovative technologies and traffic data sources provide great potential to extend advanced strategies and methods in travel behaviour research. Considering the increasing availability of real-time vehicle trajectory data and stimulated by the advances in the modelling and analysis of big data, this paper developed a hybrid unsupervised deep learning model to study driving bahaviour and risk patterns. The approach combines Autoencoder and Self-organized Maps (AESOM), to extract latent features and classify driving behaviour. The specialized neural networks are applied to data from 4032 observations collected from Global Positioning System (GPS) sensors in Shenzhen, China. In two case studies, improper vehicle lateral position maintenance, speeding and inconsistent or excessive acceleration and deceleration have been identified. The experiments have shown that back propagation through multi-layer autoencoders is effective for non-linear and multi-modal dimensionality reduction, giving low reconstruction errors from big GPS datasets.

Suggested Citation

  • Jingqiu Guo & Yangzexi Liu & Lanfang Zhang & Yibing Wang, 2018. "Driving Behaviour Style Study with a Hybrid Deep Learning Framework Based on GPS Data," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2351-:d:156561
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    References listed on IDEAS

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    1. Mozolin, M. & Thill, J. -C. & Lynn Usery, E., 2000. "Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation," Transportation Research Part B: Methodological, Elsevier, vol. 34(1), pages 53-73, January.
    2. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
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    Cited by:

    1. Pengcheng Fan & Jingqiu Guo & Haifeng Zhao & Jasper S. Wijnands & Yibing Wang, 2019. "Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    2. Dongwei Qiu & Hao Xu & Dean Luo & Qing Ye & Shaofu Li & Tong Wang & Keliang Ding, 2020. "A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-23, January.

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