Volume 41 Issue 5
Oct.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(5): 43-53
WANG Chaojian, ZHANG Daowen, JIANG Jun, XIAO Le. An Analysis of Fatal Accident Rates of Passenger Cars on Urban Roads Considering Imbalanced Data Samples[J]. Journal of Transport Information and Safety, 2023, 41(5): 43-53. doi: 10.3963/j.jssn.1674-4861.2023.05.005
Citation: WANG Chaojian, ZHANG Daowen, JIANG Jun, XIAO Le. An Analysis of Fatal Accident Rates of Passenger Cars on Urban Roads Considering Imbalanced Data Samples[J]. Journal of Transport Information and Safety, 2023, 41(5): 43-53. doi: 10.3963/j.jssn.1674-4861.2023.05.005
shu

An Analysis of Fatal Accident Rates of Passenger Cars on Urban Roads Considering Imbalanced Data Samples

doi: 10.3963/j.jssn.1674-4861.2023.05.005
  • 1.

    School of Automobile and Transportation, Xihua University, Chengdu 610039, China

  • 2.

    School of Engineering and Technology, Sichuan Sanhe College of Professionals, Luzhou 646200, Sichuan, China

  • 3.

    Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China

  • 4.

    Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chengdu 610039, China

  • Received Date: 2022-03-29
    Available Online: 2024-01-18
    Abstract
  • Traffic accidents on urban roads are frequent, and there is a significant imbalance in accident data. The coupling between different factors caused great challenges in analyzing the fatal accident rate of passenger vehicles on urban roads. Therefore, a three-stage method that integrating resampling, Bayesian networks (BN) and association rule method (ARM) is proposed. Based on the data of 1105 urban road passenger car accidents from the National Automobile Accident In-Depth Investigation System (NAIS), the BN model is constructed by selecting 16 potential feature variables from four aspects: driver, vehicle, roadway and environment. Considering the problem that the imbalance of accident types can lead to the degradation performance of BN model. Proposed data re-sampling using Synthetic Minority Over-sampling Technique (SMOTE) and Cluster Centroids (CC) before the construction of BN model. Compare the comprehensive performance of different BN models under various sampling techniques. Finally, based on the optimal BN model and combined with the ARM, the effects of different influencing factors and the coupling effect of factors on the fatal accident rate were analyzed. The results show that re-sampling method can significantly improve the comprehensive performance of BN models and the ability to identify risk factors. Among them, the BN model constructed by SMOTE sampling technique combined with GTT algorithm has the highest AUC of 0.793. Besides, compared with the BN model constructed by the original imbalanced data, the BN model constructed by SMOTE sampling explores six more risk factors. The highest fatal accident rate was 80.4% when "motorized two/three-wheelers"are coupled with"speeding". The next highest fatal accident rate is 77.4% when "motorized two/three wheelers"is coupled with"blind spots in the field of vision". Passenger cars are prone to crash with cars when turning left at the Four-Way Intersection, but the fatal accident rate is less than 20%. This method can reduce the influence of data imbalance on the analysis of road traffic accidents, and realize the analysis of the coupling effect of risk factors, thus preventing and reducing the occurrence of fatal accidents on urban roads.

     

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