An Algorithm for Incident Detection Using Artificial Neural Networks
Vehicular accidents cause tragic loss of lives and traffic congestion to the transportation system. Therefore, prompt detection of traffic incidents offers tremendous benefits of minimizing congestion and reducing secondary accidents. Most incident management systems use inductive loop detectors for...
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doaj-c6d7ac559407498596c4b60f6c6b6a542020-11-25T02:40:03ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372019-11-0162225162167An Algorithm for Incident Detection Using Artificial Neural NetworksYong-Kul Ki0Woo-Teak Jeong1Hee-Je Kwon2Mi-Ra Kim3Road Traffic Authority, Seoul City, Republic of KoreaRoad Traffic Authority, Seoul City, Republic of KoreaRoad Traffic Authority, Seoul City, Republic of KoreaRoad Traffic Authority, Seoul City, Republic of KoreaVehicular accidents cause tragic loss of lives and traffic congestion to the transportation system. Therefore, prompt detection of traffic incidents offers tremendous benefits of minimizing congestion and reducing secondary accidents. Most incident management systems use inductive loop detectors for incident detection. Inductive loops are the most commonly used traffic detectors and they collect data such as vehicle speed at a point. However, the implemented algorithms using loop detectors showed mixed success. I think that the changes in average traffic speed in case of traffic incidents have certain patterns that are different from the normal conditions. In this paper, I try to automatically detect traffic incidents using artificial neural networks and traffic condition information of the traffic information center. In the field tests, the new model performed better than existing methohttps://fruct.org/publications/fruct25/files/Ki.pdf traffic incident detectionartificial neural networkstravel speedtraffic information center |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yong-Kul Ki Woo-Teak Jeong Hee-Je Kwon Mi-Ra Kim |
spellingShingle |
Yong-Kul Ki Woo-Teak Jeong Hee-Je Kwon Mi-Ra Kim An Algorithm for Incident Detection Using Artificial Neural Networks Proceedings of the XXth Conference of Open Innovations Association FRUCT traffic incident detection artificial neural networks travel speed traffic information center |
author_facet |
Yong-Kul Ki Woo-Teak Jeong Hee-Je Kwon Mi-Ra Kim |
author_sort |
Yong-Kul Ki |
title |
An Algorithm for Incident Detection Using Artificial Neural Networks |
title_short |
An Algorithm for Incident Detection Using Artificial Neural Networks |
title_full |
An Algorithm for Incident Detection Using Artificial Neural Networks |
title_fullStr |
An Algorithm for Incident Detection Using Artificial Neural Networks |
title_full_unstemmed |
An Algorithm for Incident Detection Using Artificial Neural Networks |
title_sort |
algorithm for incident detection using artificial neural networks |
publisher |
FRUCT |
series |
Proceedings of the XXth Conference of Open Innovations Association FRUCT |
issn |
2305-7254 2343-0737 |
publishDate |
2019-11-01 |
description |
Vehicular accidents cause tragic loss of lives and traffic congestion to the transportation system. Therefore, prompt detection of traffic incidents offers tremendous benefits of minimizing congestion and reducing secondary accidents. Most incident management systems use inductive loop detectors for incident detection. Inductive loops are the most commonly used traffic detectors and they collect data such as vehicle speed at a point. However, the implemented algorithms using loop detectors showed mixed success. I think that the changes in average traffic speed in case of traffic incidents have certain patterns that are different from the normal conditions. In this paper, I try to automatically detect traffic incidents using artificial neural networks and traffic condition information of the traffic information center. In the field tests, the new model performed better than existing metho |
topic |
traffic incident detection artificial neural networks travel speed traffic information center |
url |
https://fruct.org/publications/fruct25/files/Ki.pdf
|
work_keys_str_mv |
AT yongkulki analgorithmforincidentdetectionusingartificialneuralnetworks AT wooteakjeong analgorithmforincidentdetectionusingartificialneuralnetworks AT heejekwon analgorithmforincidentdetectionusingartificialneuralnetworks AT mirakim analgorithmforincidentdetectionusingartificialneuralnetworks AT yongkulki algorithmforincidentdetectionusingartificialneuralnetworks AT wooteakjeong algorithmforincidentdetectionusingartificialneuralnetworks AT heejekwon algorithmforincidentdetectionusingartificialneuralnetworks AT mirakim algorithmforincidentdetectionusingartificialneuralnetworks |
_version_ |
1724783377221943296 |