Automated accident detection at intersections.

Zhang, Yunlong; Bruce, Lori M.; Mississippi State University

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Automated accident detection at intersections.

2004-03-01
By Zhang, Yunlong ; Bruce, Lori M.

[PDF-301.90 KB]

English

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Details:

Creators:

Zhang, Yunlong ; Bruce, Lori M.

Zhang, Yunlong ; Bruce, Lori M. Less -
Corporate Creators:

Mississippi State University
Corporate Contributors:

Mississippi. Dept. of Transportation ; Mississippi Transportation Research Center

Mississippi. Dept. of Transportation ; Mississippi Transportation Research Center Less -
Subject/TRT Terms:

[+]

Automatic Incident Detection Incident Detection Interchanges And Intersections Mathematical Models Roads Roads--Mississippi--Interchanges And Intersections--Mathematical Models Traffic Accidents--Mathematical Models Traffic Crashes Traffic Surveillance
Publication/ Report Number:

FHWA/MS-DOT-RD-04-150 ; MS-DOT-RD-04-150

FHWA/MS-DOT-RD-04-150 ; MS-DOT-RD-04-150 Less -
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.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:2ee8a8d623d78a605a0d4a28a4e166270e356131881569f1c7f843a2fd665af9
Download URL:

https://rosap.ntl.bts.gov/view/dot/24144/dot_24144_DS1.pdf
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Automated accident detection at intersections.

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