Automated accident detection at intersections.

Details

• Creators:
  Zhang, Yunlong ; Bruce, Lori M.
• Corporate Creators:
  Mississippi State University
• Corporate Contributors:
  Mississippi. Dept. of Transportation ; Mississippi Transportation Research Center
• Subject/TRT Terms:
  • Automatic incident detection
  • Incident detection
  • Traffic surveillance
  • Traffic crashes
  • Interchanges and intersections
  • Mathematical models
  • Roads
  • Traffic accidents--Mathematical models
  • Roads--Mississippi--Interchanges and intersections--Mathematical models
• Publication/ Report Number:
  FHWA/MS-DOT-RD-04-150 ; MS-DOT-RD-04-150
• Resource Type:
  Tech Report
• Geographical Coverage:
  Mississippi
• Edition:
  Final report.
• Corporate Publisher:
  Mississippi State University
• Abstract:
  This research aims to provide a timely and accurate accident detection method at intersections, which is ; very important for the Traffic Management System(TMS). This research uses acoustic signals to detect ; accident at intersections. A system is constructed that can be operated in two modes: two-class and multiclass. ; The input to the system is a three-second segment of audio signal. The output of the two-class mode ; is a label of “crash” or “non-crash”. In the multi-class mode of operation, the system identifies crashes as ; well as several types of non-crash incidents, including normal traffic and construction sounds. The system ; is composed of three main signal processing stages: feature extraction, feature reduction, and feature ; classification. Five methods of feature extraction are investigated and compared; these are based on the ; discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstral transform, and ; mel frequency cepstral transform. Statistical methods are used for feature optimization and classification. ; Three types of classifiers are investigated and compared: the nearest mean, maximum likelihood, and ; nearest neighbor methods. This study focuses on the detection algorithm development. Lab testing of the ; algorithm showed that the selected algorithm can detect intersection accidents with very high accuracy.
  More +
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24144/dot_24144_DS1.pdf
• File Type:
  [PDF - 301.90 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:0d61cc256c669cdcd274c470943de488a94904114e43bd8e250f245f206de8bd550e3d83c9b26d4f7d16a755db08671ceb30c4516203dbf199b8fd4b9c5e257c