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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Development and Exploration of Safety Performance Functions Using Multiple Modeling Techniques : Trumpet Ramps

다양한 통계 기법을 활용한 안전성능함수 개발 및 비교 연구 : 트럼펫형 램프를 중심으로

  • Yang, Samgyu (Dept. of Smart City Eng., Univ. of Hanyang) ;
  • Park, Juneyoung (Dept. of Transportation&Logistics Eng., Univ. of Hanyang/Dept. of Smart City Eng., Univ. of Hanyang) ;
  • Kwon, Kyeongjoo (Korea Expressway Corporation Gwangju Jeonnam Regional) ;
  • Lee, Hyunsuk (Korea Expressway Corporation Research Institute)
  • 양삼규 (한양대학교 스마트시티공학과) ;
  • 박준영 (한양대학교 교통물류공학과/한양대학교 스마트시티공학과) ;
  • 권경주 (한국도로공사 광주전남본부 함평지사) ;
  • 이현석 (한국도로공사 도로교통연구원)
  • Received : 2021.07.30
  • Accepted : 2021.09.28
  • Published : 2021.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In recent times, several studies have been conducted focusing on crashes occurring on the main segment of the highway. However, there is a dearth of research dealing with traffic safety relating to other highway facilities, especially ramp areas. According to the Korea Expressway Corporation's Expressway Information Service, 6,717 crashes have occurred on ramps in the five years from 2015~2019, which accounts for about 15% of all highway accidents. In this study, the simple and full safety performance functions (SPFs) were evaluated and explored using different statistical distributions (i.e., Poisson Gamma (PG) and Poisson Inverse Gaussian (PIG)) and techniques (i.e., fixed effects (FE) and random effects (RE)) to provide more accurate crash prediction models for highway ramp sections. Data on the geometric characteristics of traffic and roadways were collected from various systems and with extensive efforts using a street-view application. The results showed that the PIG models present more accurate crash predictions in general. The results also indicated that the RE models performed better than FE models for simple and full SPFs. The findings from this study offer transportation practitioners using the Korea Expressway Corporation's Expressway a dependable reference to enhance and understand traffic safety in ramp areas based on accurate crash prediction models and empirical evidence.

최근 고속도로 본선구간에서 발생한 교통사고에 대한 연구가 다수 수행되고 있으나, 램프와 같이 본선 외 구간에 대한 교통안전을 다루는 연구는 미미한 실정이다. 최근 5년(2015년~2019년)동안 램프에서 발생한 사고는 총 6,717건으로 이는 전체 고속도로 사고의 약 15%를 차지한다. 본 연구에서는 고속도로 램프구간에 대해 보다 정확한 사고 예측 모형을 제공하기 위해 포아송 감마(PG)와 포아송 역가우스(PIG)와 같은 다양한 통계 분포를 비롯하여 랜덤효과와 같은 기법을 적용하여 Simple 및 Full SPF를 구축하고 비교하였다. 교통 및 도로 기하구조 데이터는 로드뷰와 같은 다양한 시스템에서 수집되었다. 분석 결과, PIG 모형은 일반적으로 더 정확한 사고 예측을 제시하며, Simpe SPF와 Full SPF 모두에서 임의효과 모형이 더욱 우수한 성능을 나타내었다. 본 연구결과는 교통실무자들에게 정확한 사고 예측 모형을 기반으로 램프구간 교통안전을 증대시키고 이해할 수 있는 참고자료로써 활용될 수 있다.

Keywords

Acknowledgement

본 연구는 한국연구재단(NRF-2019R1G1A1010209)의 지원으로 수행하였습니다.

References

  1. Alarifi S. A., Abdel-Aty M. A., Lee J. and Park J.(2017), "Crash modeling for intersections and segments along corridors: A Bayesian multilevel joint model with random parameters," Analytic Methods in Accident Research, vol. 16, pp.48-59. https://doi.org/10.1016/j.amar.2017.08.002
  2. American Association of State Highways and Transportation Officials(AASHTO)(2010), The Highway Safety Manual.
  3. Anastasopoulos P. C., Mannering F. L., Shankar V. N. and Haddock J. E.(2012), "A study of factors affecting highway accident rates using the random-parameters tobit model," Accident Analysis & Prevention, vol. 45, pp.628-633. https://doi.org/10.1016/j.aap.2011.09.015
  4. Chin H. C. and Quddus M. A.(2003), "Applying the Random Effect Negative Binomial Model to Examine Traffic Accident Occurrence at Signalized Intersections," Accident Analysis & Prevention, vol. 35, no. 2, pp.253-259. doi: 10.1016/S0001-4575(02)00003-9
  5. Choi S. R. N., Kim M. J., Oh C. and Lee K. Y.(2013), "Effects of Weather and Traffic Conditions on Truck Accident Severity on Freeways," J. Korean Soc. Civ. Eng., vol. 33, no. 3, pp.1105-1113. https://doi.org/10.12652/Ksce.2013.33.3.1105
  6. Choi Y. H., Oh Y. T., Choi K. C., Lee C. K. and Yun I. S.(2012), "Traffic Crash Prediction Models for Expressway Ramps," Int. J. Highw. Eng., vol. 14, no. 5, pp.133-143. https://doi.org/10.7855/IJHE.2012.14.5.133
  7. Dean C., Lawless J. F. and Willmot G. E.(1989), "A mixed poisson-inverse-gaussian regression model," Canadian Journal of Statistics, vol. 17, no. 2, pp.171-181. https://doi.org/10.2307/3314846
  8. Hauer E.(1997), Observational Before-After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety, Oxford, UK: Pergamon Press, Elsevier Science Ltd.
  9. Kang C. M., Chung Y. S. and Chang Y. J.(2019), "Injury Severity Analysis of Truck-involved Crashes on Korean Freeway Systems using an Ordered Probit Model," J. Korean Soc. Civ. Eng., vol. 39, no. 3, pp.391-398. https://doi.org/10.12652/KSCE.2019.39.3.0391
  10. Kang J. G. and Lee S. G.(2002), "Traffic Accident Prediction Model by Freeway Geometric Types," J. Korean Soc. Transp., vol. 20, no. 4, pp.163-175.
  11. Kim S. Y., Choi J. S., Lee S. B., Kim S. M., Cho W. B. and Kim Y. S.(2011), "Freeway Crash Frequency Model Development Based on the Classification of Geometric Alignment Type," Int. J. Highw. Eng., vol. 13, no. 1, pp.97-105. https://doi.org/10.7855/IJHE.2011.13.1.097
  12. Kulmala R.(1995), Safety at Rural Three-and Four-arm Junctions: Development and Application of Accident Prediction Models, Espoo: Technical Research Center at Espoo, VTT Publications.
  13. Lee S. H., Park M. H. and Woo Y. H.(2015), "A study on developing crash prediction model for urban intersections considering random effects," The Journal of The Korea Institute of Intelligent Transport Systems, vol. 14, no. 1, pp.85-93. https://doi.org/10.12815/kits.2015.14.1.085
  14. Lemp J. D., Kockelman K. M. and Unnikrishnan A.(2011), "Analysis of Large Truck Crash Severity Using Heteroskedastic Ordered Probit Models," Accid. Anal. Prev., vol. 43, no. 1, pp.370-380. https://doi.org/10.1016/j.aap.2010.09.006
  15. Ministry of Land, Infrastructure and Transport(2013), Rules concerning road structure and facilities. (in Korean)
  16. Park J., Abdel-Aty M. and Lee C.(2014), "Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways," Accident Analysis & Prevention, vol. 70, pp.167-177. https://doi.org/10.1016/j.aap.2014.03.016
  17. Rigby R. A. and Stasinopoulos D. M.(2005), "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 54, no. 3, pp.507-554. https://doi.org/10.1111/j.1467-9876.2005.00510.x
  18. Srinivasan R., Carter D. and Bauer K. M.(2013), Safety performance function decision guide: SPF calibration vs SPF development (No. FHWA-SA-14-004), United States, Federal Highway Administration, Office of Safety.
  19. Yoon B. J.(2007), "Development of Traffic Accident Forecasting Models for Trumpet IC Ramp Types of Freeway," J. Korean Soc. Civ. Eng., vol. 27, no. 1D, pp.81-87.
  20. Yu R., Abdel-Aty M. and Ahmed M.(2013), "Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors," Accident Analysis & Prevention, vol. 50, pp.371-376. https://doi.org/10.1016/j.aap.2012.05.011
  21. Zha L., Lord D. and Zou Y.(2016), "The Poisson inverse Gaussian(PIG) generalized linear regression model for analyzing motor vehicle crash data," Journal of Transportation Safety & Security, vol. 8, no. 1, pp.18-35. https://doi.org/10.1080/19439962.2014.977502