Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Study on Application of Autonomous Traffic Information Based on Artificial Intelligence

인공지능 기반의 자율형 교통정보 응용에 대한 연구

  • Oh, Am-Suk (Department of Digital Media Engineering, Tongmyong University)
  • Received : 2022.04.21
  • Accepted : 2022.04.25
  • Published : 2022.06.30
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Abstract

This study aims to prevent secondary traffic accidents with high severity by overcoming the limitations of existing traffic information collection systems through analysis of traffic information collection detectors and various algorithms used to detect unexpected situations. In other words, this study is meaningful present that analyzing the 'unexpected situation that causes secondary traffic accidents' and 'Existing traffic information collection system' accordingly presenting a solution that can preemptively prevent secondary traffic accidents, intelligent traffic information collection system that enables accurate information collection on all sections of the road. As a result of the experiment, the reliability of data transmission reached 97% based on 95%, the data transmission speed averaged 209ms based on 1000ms, and the network failover time achieved targets of 50sec based on 120sec.

본 연구는 교통정보를 수집하기 위한 검지기 와 돌발 상황 검지에 사용되는 다양한 알고리즘들의 분석을 통해 기존 교통정보 수집체계의 한계를 극복하여 심각도가 높은 2차 교통사고를 예방하고자 한다. 즉 본 연구는 2차 교통사고를 유발하는 돌발 상황과 기존 교통정보 수집체계를 분석하고 그에 알맞은 2차 교통사고의 선제적 예방이 가능한 솔루션 및 도로 전 구간에 대한 정확한 정보수집이 가능한 지능화된 새로운 교통정보 수집 및 전달체계를 제시한다. 실험결과 데이터 전송 신뢰도는 95% 기준 97%를, 데이터 전송 속도는 1000ms 기준 평균 209ms, 네트워크 장애복구 시간은 120sec 기준 50sec의 목표치를 달성하였다.

Keywords

Acknowledgement

This Research was supported by the Tongmyong University Research Grants 2021(2021A032).

References

  1. J. K. Kim, R. Seo, J. Gim, G. Yu, Y. Gim, D. Hwang, D. Gim, H. Bak, "Estimation and evaluation of road traffic accident costs in 2018 [2019 edition]" Korea Road Traffic Authority, Safety headquarters traffic accident comprehensive analysis center, Statistical Report 2019-0237-053, 2019.
  2. Traffic Accident Analysis System(TAAS). Traffic accident detailed statistics [Internet], accessed Jun 23.2020. Available: http://taas.koroad.or.kr/web/shp/sbm/.initUnityAnalsSys.do?menuId=WEB_KMP_OVT_UAS.
  3. Korea Road Traffic Authority. (2018, April). Power to prevent secondary highway accidents, first evacuate. [Press Release]. last modified Apr 17.2018, accessed Jun 23.2020. Available: http://www.ex.co.kr/site/com/pageProcess.do?oneDpMnNo=100500000&selDpPgNm=menuno%3D100501020&selDpMnNo=100501020¶mStr=&detailTp=&dirNm=portal&kwd=.
  4. E. J. Kim, S. G. Kwak, K. S. Bae, and Y. J. An, "Research on Traffic Information Management System data quality diagnosis and evaluation system construction," Korea Road Traffic Authority, Traffic Science Researcher, Research Report 2014-0115-103, 2014.
  5. H. J. Payne and S. C. Tignor, "Freeway Incident-Detection Algorithms Based on Decision Trees with States," in Transportation Research Record: Journal of the Transportation Research Board, no. 682, pp. 30-37, 1978.
  6. Y. S. Jeong, M. Castro-Neto, M. K. Jeong & L. Han, "A Wavelet-Based Freeway Incident Detection Algorithm with Adapting Threshold Parameters," Transportation Research Part C, vol.19, no.1, pp. 1-19, 2011. https://doi.org/10.1016/j.trc.2009.10.005
  7. Y. K. Ki, J. W. Choi, Y. J. An, Y. J. Kim, "Research on the improvement of traffic information quality at the Central Traffic Information Center," Korea Road Traffic Authority, Traffic Science Researcher, Research Report 2014-0106-104, 2014.
  8. Y. K. Ki, "A Traffic Incident Detection Model Using Artificial Neural Networks," Journal of Next-generation Convergence Technology Association, vol. 4, no. 1, pp. 47-54, Feb. 2020. https://doi.org/10.33097/JNCTA.2020.04.01.47