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Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

Author

Listed:
  • Pengcheng Fan

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Jingqiu Guo

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Haifeng Zhao

    (Transport, Health and Urban Design Research Hub, Melbourne School of Design, The University of Melbourne, Melbourne 3052, Australia)

  • Jasper S. Wijnands

    (Transport, Health and Urban Design Research Hub, Melbourne School of Design, The University of Melbourne, Melbourne 3052, Australia)

  • Yibing Wang

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

Abstract

Although a lot of work has been conducted on car-following modeling, model calibration and validation are still a great challenge, especially in the era of autonomous driving. Most challengingly, besides the immediate benefit incurred with a car-following action, a smart vehicle needs to learn to evaluate the long-term benefits and become foresighted in conducting car-following behaviors. Driving memory, which plays a significant role in car-following, has seldom been considered in current models. This paper focuses on the impact of driving memory on car-following behavior, particularly, historical driving memory represents certain types of driving regimes and drivers’ maneuver in coordination with the variety of driving regimes. An autoencoder was used to extract the main features underlying the time-series data in historical driving memory. Long short-term memory (LSTM) neural network model has been employed to investigate the relationship between driving memory and car-following behavior. The results show that velocity, relative velocity, instant perception time (IPT), and time gap are the most relevant parameters, while distance gap is insignificant. Furthermore, we compared the accuracy and robustness of three patterns including various driving memory information and span levels. This study contributes to bridging the gap between historical driving memory and car-following behavior modeling. The developed LSTM methodology has the potential to provide personalized warnings of dangerous car-following distance over the next second.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6755-:d:291988
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    References listed on IDEAS

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    Cited by:

    1. Navin Ranjan & Sovit Bhandari & Pervez Khan & Youn-Sik Hong & Hoon Kim, 2021. "Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder," Sustainability, MDPI, vol. 13(9), pages 1-26, May.

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