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

Adeli, Hojjat; Ohio State University. Department of Civil and Environmental Engineering and Geodetic Science

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

i

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

2001-04-01
By Adeli, Hojjat

[PDF-81.40 KB]

English

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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

Ohio. Dept. of Transportation ; United States. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Advanced Traffic Management Systems Disabled Vehicles On Express Highways--Ohio Incident Management Neural Networks (Computer Science)--Ohio Traffic Congestion--Ohio--Prevention
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)
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:a3f51b0bd4474a5915588ea03aeb2f87c87faa14d08d4c6559669f7d62eb4c54
Download URL:

https://rosap.ntl.bts.gov/view/dot/17580/dot_17580_DS1.pdf
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Neural network model for automatic traffic incident detection : executive summary.

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