DOI QR코드

DOI QR Code

Methodology for Calculating Surrogate Safety Measure by Using Vehicular Trajectory and Its Application

차량궤적자료를 이용한 SSM 산출 방법론 개발과 적용사례 분석

  • PARK, Seongyong (Department of Road Transport Research, The Korea Transport Institute) ;
  • LEE, Chungwon (Department of Civil and Environmental Engineering, Seoul National University) ;
  • KHO, Seung-Young (Department of Civil and Environmental Engineering, Seoul National University) ;
  • LEE, Yong-Gwan (Institute of Construction and Environment Engineering, Seoul National University)
  • 박성용 (한국교통연구원 도로교통본부) ;
  • 이청원 (서울대학교 건설환경공학부) ;
  • 고승영 (서울대학교 건설환경공학부) ;
  • 이용관 (서울대학교 건설환경종합연구소)
  • Received : 2014.09.03
  • Accepted : 2015.07.30
  • Published : 2015.08.31

Abstract

Estimating the risks on the roadway using surrogate safety measures (SSM) has an advantage in that it focuses on the vehicle trajectory directly involved in conflicts. On the other hand, there is a restriction on estimating the risks of continuous segments due to the limited data collected from a location. To overcome the restriction, this study presents the scheme of acquiring the vehicular trajectory using real time kinematics-differential global positioning system (RTK-DGPS) and develops a methodology which contains the considerations of the problems to calculate the SSM such as time-to-collision (TTC), deceleration rate to avoid collision (DRAC) and acceleration noise (AN). By using the methodology, this study shows a result from an experiment executed in a section where the variation of vehicular movement can be observed from several continuous flow roadway sections near Seoul and Gyeonggi Province in Korea. The result illustrated the risks on the roadway by the SSM metrics in certain situations like merging and diverging, stop-and-go, and weaving. This study would be applied to relate the dangers with characteristics of drivers and roadway sections, and prevenst accidents or conflicts by detecting dangerous roadway sections and drivers' behaviors. This study contributes to improving roadway safety and reducing car-accidents.

Surrogate safety measure(SSM)를 이용하여 도로상의 위험을 측정하는 방식은 사고의 직접적인 원인과 연관된 차량의 거동을 분석 대상으로 한다는 장점을 가지고 있으나, 한 지점에 국한된 정보를 이용하기 때문에 위험을 연속적으로 분석하는 데에 제약이 있다. 이러한 한계를 극복하기 위해 본 연구에서는 RTK-DGPS를 이용하여 차량들의 궤적을 얻는 방안을 제시하고, 이를 통해 time-to-collision(TTC), deceleration rate to avoid collision(DRAC), acceleration noise(AN) 등의 SSM을 산출하는 데에 필요한 문제들을 고려한 방법론을 설정하였다. 또한 본 연구에서는 검토된 방법론을 이용하여 운전 중 발생하는 다양한 차량거동 변화를 관찰하기 위하여 영동고속도로 북수원IC-군포IC 구간을 대상으로 실험을 수행한 결과를 제시하였다. 그 결과 궤적 기반 SSM 지표값이 다양한 주행 상황에서의 위험성을 합리적으로 설명할 수 있음을 확인하였다. 본 연구는 향후 다양한 구간특성 및 운전자 특성에 따른 위험상황을 설명하는 연구를 수행하고, 위험구간 감지 및 위험한 운전행태의 감지를 통한 사고예방에 활용될 수 있을 것으로 판단되며, 이를 통하여 위험도로 개선 및 교통사고 줄이기 사업을 효율적으로 추진하는 데에 기여할 수 있을 것이다.

Keywords

References

  1. Almquist S., Hyden C., Risser R. (1991), Use of Speed Limiters in Cars for Increased Safety and a Better Environment, TRR, 1318, 34-39.
  2. American Association of State Highway and Transportation Officials (2004), A Policy on Geometric Design of Highways and Streets, AASHTO, Washington, D.C.
  3. Archer J. (2005), Methods for the Assesment and Prediction of Traffic Safety at Urban Intersection and Their Application in Micro-simulation Modeling, PhD Thesis, Dept. of Infrastructure, Royal Institute of Technology, Sweden.
  4. Chin H. C., Quek S. T. (1997), Measurement of Traffic Conflicts, Safety Science, 26(3), 169-185. https://doi.org/10.1016/S0925-7535(97)00041-6
  5. Cooper D. F., Ferguson N. (1976), Traffic Studies at T-junctions-a Conflict Simulation Model, Tfc Eng. and Ctrl., 17, 306-309.
  6. Cunto F. J. (2008), Assessing Safety Performance of Transportation Systems Using Microscopic Simulation, Ph.D Thesis, Dept. of Civil Eng., Univ. of Waterloo, Ontario, Canada.
  7. Drew D. R., Dudek C. L., Keese C. J. (1967), Freeway Level of Service as Described by an Energy-Acceleration Noise Model, TRR, 162, 30-85.
  8. Gettman D., Head L. (2003), Surrogate Safety Measures From Traffic Simulation Models, TRR, 1840, 104-115.
  9. Google Inc. (2013), Google Earth (Build 7.1.2.2041)
  10. F., Gallelli V. (2012), Estimation of Safety Performance Measures From Smartphone Sensors, Procedia - Soc. and Behavioral Sci. 54, 1095-1103. https://doi.org/10.1016/j.sbspro.2012.09.824
  11. Hayward J. (1972), Near Miss Determination through Use of a Scale of Danger, Report No.TTSC 7115, The Pennsylvania State University, Pennsylvania.
  12. Herman R., Montroll E. W., Potts R. B., Rothery R. W. (1959), Traffic Dynamics: Analysis of Stability in Car-following, Opns Res, 7(1), 86-106. https://doi.org/10.1287/opre.7.1.86
  13. Hirst S. J., Graham R. (1997), The Format and Presentation of Collision Warnings, Ergonomics and safety of intelligent driver interfaces, 203-219.
  14. Hourdakis J., Garg V., Michalopoulos P. G., Davis G. A. (2006), Real-time Detection of Crash-prone Conditions at Freeway High Crash Locations, TRR, 1968, 83-91.
  15. KICT (2014), Annual Traffic Volume Report for 2013, Ministry of Land, Infrastructure and Transport, Sejong-si, Republic of Korea, 111.
  16. Lee J. D., McGehee D. V., Brown T. L., Reyes M. L. (2002), Collision Warning Time, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator, Human Factors, 44(2), 314-334. https://doi.org/10.1518/0018720024497844
  17. Minderhoud M. M., Bovy P. H. L. (2001), Extended Time-to-collision Measures for Road Traffic Safety Assessment, Accid Anal Prev, 33(1), 89-97. https://doi.org/10.1016/S0001-4575(00)00019-1
  18. Oh C., Cho J. I., Kim J. H., Oh J. T. (2007), Methodology for Evaluating Real-time Rear-end Collision Risks Based on Vehicle Trajectory Data Extracted From Video Image Tracking, J. Korean Soc. Transp., 25(5), Korean Society of Transportation, 173-182.
  19. Oh C., Kim T. (2010), Estimation of Rear-end Crash Potential Using Vehicle Trajectory Data, Accid Anal Prev, 42(6), 1888-1893. https://doi.org/10.1016/j.aap.2010.05.009
  20. Ozbay K., Yang H., Bartin B., Mudigonda S. (2008), Derivation and Validation of New Simulation-Based Surrogate Safety Measure, TRR, 2083, 105-113.
  21. Saccomanno F., Cunto F., Guido G., Vitale A. (2008), Comparing Safety at Signalized Intersections and Roundabouts Using Simulated Traffic Conflicts, TRR, 2078, 90-95.
  22. Sayed T., Brown G., Navin F. (1994), Simulation of Traffic Conflicts at Unsignalized Intersections With TSC-Sim, Accid Anal Prev, 26(5), 593-607. https://doi.org/10.1016/0001-4575(94)90021-3
  23. Sayed T., Zein S. (1999), Traffic Conflict Standards for Intersections, Transportation Planning and Technology, 22(4), 309-323. https://doi.org/10.1080/03081069908717634
  24. Touran A., Brackstone M. A., McDonald M. (1999), A Collision Model for Safety Evaluation of Autonomous Intelligent Cruise Control, Accid Anal Prev, 31, 567-578. https://doi.org/10.1016/S0001-4575(99)00013-5
  25. van der Horst R. (1991), Time-to-collision as a Cue for Decision-making in Braking, Vision in Vehicles III, Elsevier Science Publishers B.V., Amsterdam, 1991, 19-26.
  26. van der Horst R., Hogema J. (1993), Time-To-Collision and Collision Avoidance Systems, 6th ICTCT workshop, Salzburg, Austria.
  27. Vogel K. (2003), A Comparison of Headway and Time to Collision as Safety Indicators, Accid Anal Prev, 35(3), 427-433. https://doi.org/10.1016/S0001-4575(02)00022-2

Cited by

  1. 도로 및 인접차량과의 상호작용분석을 통한 차량의 주행안전성 평가기법 개발 연구 vol.35, pp.2, 2017, https://doi.org/10.7470/jkst.2017.35.2.116
  2. 초고속 주행환경에서의 진출입 시설간 적정 이격거리 기준 산정 연구 vol.19, pp.3, 2015, https://doi.org/10.7855/ijhe.2017.19.3.045