The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Spatiotemporal Traffic Density Estimation Based on Low Frequency ADAS Probe Data on Freeway

표본 ADAS 차두거리 기반 연속류 시공간적 교통밀도 추정

  • Lim, Donghyun (Advanced Institute of Convergence Technology) ;
  • Ko, Eunjeong (Dept. of The Cho Chun Shik Graduate School for Green Transportation, KAIST) ;
  • Seo, Younghoon (Advanced Institute of Convergence Technology) ;
  • Kim, Hyungjoo (Advanced Institute of Convergence Technology)
  • 임동현 (차세대융합기술연구원 첨단교통체계연구실) ;
  • 고은정 (한국과학기술원 조천식녹색교통대학원) ;
  • 서영훈 (차세대융합기술연구원 첨단교통체계연구실) ;
  • 김형주 (차세대융합기술연구원 첨단교통체계연구실)
  • Received : 2020.08.19
  • Accepted : 2020.10.23
  • Published : 2020.12.31
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Abstract

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

본 연구는 첨단운전자보조시스템(Advanced Driver Assistance System, ADAS)이 빠르게 보급됨에 따라 표본 프로브 차량에 설치된 ADAS로부터 얻은 개별차량의 궤적 데이터와 전방차량과의 차두거리 데이터를 이용하여 연속류의 교통밀도를 추정 및 분석하는 것을 목적으로 한다. 과거 연속류 교통밀도는 주로 차량검지시스템(Vehicle Detection System, VDS)에서 수집되는 교통량, 속도, 점유율 등의 데이터를 가공하여 추정되거나, CCTV등의 영상정보를 활용하여 직접 차량 대수를 계수하여 추정되었다. 이러한 방식은 교통밀도 추정의 공간적 제약이 있고, 교통 혼잡시 추정의 신뢰도가 낮다는 한계를 보였다. 이에 본 연구에서는 선행연구의 한계를 극복하기 위해 ADAS로부터 수집된 개별차량 궤적 데이터와 차두거리 정보를 활용하여 도로의 공간을 검지하고 일반화된 밀도(Generalized Density)방식을 이용하여 시공간적 교통밀도를 추정한다. 이에 따라 ADAS차량의 표본율에 따른 교통밀도 추정의 정확도를 분석한 결과, 30%의 표본율일 경우 교통밀도 참 값과 약 90% 일치하는 것으로 나타났다. 이를 통해 본 연구는 향후 ADAS 및 자율주행차량이 혼재되는 도로 상황에서 신뢰도 높은 교통밀도 추정을 가능하게 하며 효율적인 교통운영관리에 기여할 수 있을 것으로 판단된다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1C1C1003296).

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