Abstract

Introduction:</jats:title><jats:p>An acoustic signal-based tunnel accident detection system was developed in this study. In a tunnel environment, the sound diffusion effect is minimized and thanks to that, discrimination of accident sounds (crash and skid) from other noises can apparently be accomplished.</jats:p></jats:sec><jats:sec><jats:title>Discussion:</jats:title><jats:p>The system is composed of three parts: algorithm, field device, and center system. To distinguish accident-related acoustic signals such as a crash or skid among various other sounds in a tunnel, a delicate algorithm that can discriminate those signals from other normal signals generated from moving vehicles was created.</jats:p></jats:sec><jats:sec><jats:title>Conclusion:</jats:title><jats:p>The developed algorithm processes acoustic signals to filter out noises and to identify accident-related signals. The field device, installed in a tunnel, collects analog sounds, transforms them into digital signals, and transmits the digital signals to the server in the tunnel traffic management center. Lastly, in the tunnel traffic management center, the acoustic signal processing algorithm described above, installed in a server system, can instantaneously detect accidents. Once confirmed by the system operators, the information on the detected accidents is intended to be provided to drivers following behind as well as relevant agencies to prevent secondary accidents and to respond promptly. The developed system was evaluated in a real tunnel environment using traffic accident sounds acquired from real crash tests. The detection rates were 95, 91, and 80% at distances of 10, 30, and 50 m, respectively with a detection duration less than 1.4 s. Compared to conventional detection systems using loop detectors or video images that have a long detection time of around 1 min, the developed system can be regarded as superior in that it has an extremely short detection time, which, of course, is one of the most important factors for automatic incident detection systems.</jats:p></jats:sec


Original document

The different versions of the original document can be found in:

https://zenodo.org/record/1436310 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
https://www.benthamopen.com/ABSTRACT/TOTJ-12-344,
https://opentransportationjournal.com/VOLUME/12/PAGE/344/PDF,
https://academic.microsoft.com/#/detail/2900483971
https://opentransportationjournal.com/contents/volumes/V12/TOTJ-12-344/TOTJ-12-344.xml,
http://dx.doi.org/10.2174/1874447801812010344 under the license https://creativecommons.org/licenses/by/4.0/legalcode
http://dx.doi.org/10.5281/zenodo.1436309 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode


DOIS: 10.5281/zenodo.1436310 10.5281/zenodo.1436309 10.2174/1874447801812010344

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.5281/zenodo.1436310
Licence: Other

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