Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 115-125
SHI Yu-chen, YAN Song, YAO Dan-ya, ZHANG Yi. SVM-LSTM-based car-following behavior recognition and information credibility discirmination[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
Citation: SHI Yu-chen, YAN Song, YAO Dan-ya, ZHANG Yi. SVM-LSTM-based car-following behavior recognition and information credibility discirmination[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
shu

SVM-LSTM-based car-following behavior recognition and information credibility discirmination

doi: 10.19818/j.cnki.1671-1637.2022.03.009
  • 1.

    School of Information Science and Technology, Tsinghua University, Beijing 100084, China

  • 2.

    Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China

  • 3.

    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southest University, Nanjing 210096, Jiangsu, China

  • 4.

    Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, Guangdong, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

More Information
    Abstract
  • To effectively improve the traffic system security by using the real-time interaction information in intelligent vehicle-infrastructure cooperative systems (i-VICS), a credibility discrimination approach for traffic information based on the traffic business features was proposed. In particular, the model for the car-following behavior recognition and the information credibility discrimination was built based on the support vector machine (SVM) and long short-term memory (LSTM) neural network. It was composed of the SVM-based car-following behavior recognition model and the LSTM neural network-based car-following speed prediction model. The feature vector representing the vehicle driving states was set, and the vehicle driving states were divided into the following and non-following by the SVM-based car-following behavior recognition model. For following vehicles, their speeds were predicted by the LSTM neural network-based car-following speed prediction model according to the history data. With the SVM-LSTM-based information credibility discrimination model, the credibility of vehicle data was judged by checking whether the difference between the predicted speed and the actual speed of the following vehicles was within the reasonable range, and in this way, the information credibility discrimination was achieved. The public dataset was employed to train and test the proposed models, and several abnormal test datasets of various abnormity types and abnormity amplitude were built to verify the SVM-LSTM neural network-based model for the car-following behavior recognition and the information credibility discrimination. Research results show that the vehicle driving behavior recognition accuracy of the SVM-based car-following behavior recognition model is up to 99%, and the predicted car-following speed precision with an order of magnitude of cm·s-1 can be achieved by the LSTM neural network-based car-following speed prediction model. The discrimination accuracy of the SVM-LSTM neural network-based model for the car-following behavior recognition and information credibility discrimination is up to 97% on the normal test datasets and multiple abnormal test datasets. Thus, the proposed approach can be applied for the real-time information credibility discriminations of road side units (RSUs) to on-board units (OBUs) and between OBUs. 8 tabs, 9 figs, 30 refs.

     

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