Neural network model for automatic traffic incident detection : executive summary.

Details

• Creators:
  Adeli, Hojjat
• Corporate Creators:
  Ohio State University. Department of Civil and Environmental Engineering and Geodetic Science
• Corporate Contributors:
  Ohio. Dept. of Transportation ; United States. Federal Highway Administration
• Subject/TRT Terms:
  • Advanced traffic management systems
  • Incident management
  • Traffic congestion--Ohio--Prevention
  • Disabled vehicles on express highways--Ohio
  • Neural networks (Computer science)--Ohio
• Publication/ Report Number:
  FHWA/HWY-03/2002
• Resource Type:
  Tech Report
• Geographical Coverage:
  Ohio
• Edition:
  Executive summary.
• Corporate Publisher:
  Ohio. Dept. of Transportation
• Abstract:
  Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelligent system approach and several innovative algorithms were developed for solution of the freeway traffic incident detection problem employing advanced signal processing, pattern recognition, and classification techniques. The methodology effectively integrates fuzzy, wavelet, and neural computing techniques to improve reliability and robustness
• Format:
  PDF
• Funding:
  14690(0)
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/17580/dot_17580_DS1.pdf
• File Type:
  [PDF - 81.40 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:a3f51b0bd4474a5915588ea03aeb2f87c87faa14d08d4c6559669f7d62eb4c54