Volume 41 Issue 3
Jun.  2023
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CHEN Junyu, LI Jinlong, XU Lunhui, WU Pan, LIN Yongjie. An Automatic Detection Method for Traffic Accidents Based on ADASYN-XGBoost[J]. Journal of Transport Information and Safety, 2023, 41(3): 12-22. doi: 10.3963/j.jssn.1674-4861.2023.03.002
Citation: CHEN Junyu, LI Jinlong, XU Lunhui, WU Pan, LIN Yongjie. An Automatic Detection Method for Traffic Accidents Based on ADASYN-XGBoost[J]. Journal of Transport Information and Safety, 2023, 41(3): 12-22. doi: 10.3963/j.jssn.1674-4861.2023.03.002
shu

An Automatic Detection Method for Traffic Accidents Based on ADASYN-XGBoost

doi: 10.3963/j.jssn.1674-4861.2023.03.002
  • 1.

    School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China

  • 2.

    IT Academy, Guangdong University of Science and Technology, Dongguan 510812, Guangdong, China

  • Received Date: 2022-09-22
    Available Online: 2023-09-16
    Abstract
  • A data-driven approach for automatic detection of road traffic accidents plays an important role in timely rescue and reducing the impact of road accidents. In order to solve the sample imbalance problem in automatic detection of traffic accidents a hybrid adaptive oversampling technique and extreme gradient boosting tree algorithm (ADASYN-XGBoost) is studied. In particular, to effectively mine the intrinsic correlation law between spatio-temporal feature of the data and accident occurrence form the unbalanced traffic accident samples. The initial combinations of feature variable are set. And to improve the quality of the training data, the adaptive synthetic oversampling method (ADASYN) is introduced to balance the number of samples between the accident class and the non-accident class. To improving the detection effect, a traffic accident detection model based on extreme gradient boosting (XGBoost) is developed, which is utilized to filter the features of the enhanced data samples. Finally, to obtain the best combination of parameters, a Bayesian optimization algorithm is used to quickly calibrate the parameters of XGBoost. In this paper, the ADASYN-XGBoost method is validated and investigated using the Portland Freeway dataset. The results show that ADASYN-XGBoost optimizes all detection metrics compared to the state-of-the-art benchmark model. The F1 score reaches 94.47% and the false detection rate is as low as 8.95%. The F1 scores of ADASYN-XGBoost are 94.47%, 88.89%, and 81.93% when the number of model training samples are 2800, 500 (18% of the initial sample size), and 150 (5% of the initial sample size). In further ablation experiments, the performance indexes of each benchmark model after equalizing positive and negative samples are improved by 2.68% to 44.85%. The method proposed in this paper can effectively solve the sample imbalance problem in detection of road traffic accidents, which also provides technical support for road traffic safety prevention and accident management.

     

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