Volume 41 Issue 1
Feb.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(1): 34-42
MA Qinglu, FU Binglin, MA Lian, LI Yangmei. A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound[J]. Journal of Transport Information and Safety, 2023, 41(1): 34-42. doi: 10.3963/j.jssn.1674-4861.2023.01.004
Citation: MA Qinglu, FU Binglin, MA Lian, LI Yangmei. A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound[J]. Journal of Transport Information and Safety, 2023, 41(1): 34-42. doi: 10.3963/j.jssn.1674-4861.2023.01.004
shu

A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound

doi: 10.3963/j.jssn.1674-4861.2023.01.004
  • 1.

    School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

  • 2.

    Ningxia Expressway Co., LTD, Yinchuan 750000, China

  • Received Date: 2022-03-07
    Available Online: 2023-05-13
    Abstract
  • In response to the need of effectively detecting traffic accidents in highway tunnel sections, a novel acoustic detection method is introduced, so as to study an intelligent way for detecting traffic accidents in tunnels based on abnormal sound. By analyzing the issues of using Short-Term Energy (STE) and Mel-scale Frequency Cepstral Coefficients (MFCC) in identifying accident sections and interfering with precision, a modified fusion feature MFCCE is proposed to detect traffic accidents in tunnel sections. The new fusion feature of the MFCCE is obtained by extracting STE and MFCC features in virtue of Principal Component Analysis (PCA) to conduct feature fusion. Based on an observed traffic accident dataset, a sample dataset of noise experiments in two tunnels containing braking and collision sounds is developed, which corresponds to the traffic scenario of morning peak hours (from 07:00 to 08:00) and regular hours (from 12:00 to 13:00) respectively. Then an endpoint detection method is utilized to validate the proposed method, which is then compared with the other two methods (STE and MFCC). The Pearson correlation coefficient is determined as the final evaluation method, through which correlation coefficients r is used to compare the positive correlation of the three test results with the original samples. Experimental results show that the correlation coefficients of STE are 0.933 and 0.988 in the regular and morning peak hours respectively; the correlation coefficients of MFCC are 0.998 in both regular and morning peak hours, while the correlation coefficient of MFCCE (0.999) is higher than the other two detection methods in both regular and morning peak hours. The average correlation coefficients of MFCCE are 3.95% and 1.00% higher than the other two detection methods, respectively.

     

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