Estimation of the Effect of Commercial Vehicle Driving Safety Training Using the Propensity Score Matching Method

Joonbeom Lim; Dongwoo Lee

doi:10.7470/jkst.2023.41.2.254

All Issue

2023 Vol.41, Issue 2 Preview Page

Article

Estimation of the Effect of Commercial Vehicle Driving Safety Training Using the Propensity Score Matching Method 성향점수매칭(PSM)방법을 이용한 교통안전체험교육 효과추정

30 April 2023. pp. 254-265

PDF XML

Abstract

Increasing traffic safety concerns have become critical issues in the realm of transport. Since a well-known roadside safety policy, Vision Zero, was originally established in Sweden in 1997, various policies and related technical treatments have been adopted to address road traffic safety issues in several countries. To enhance the effectiveness of the safety policy, the potential impacts of these policies and treatments on traffic accident prevention are required to be evaluated. Nonetheless, there are some limitations in quantifying the effects of the safety policy due to the limitation of data availability and methodological issues. Although existing studies have adopted statistical methodologies such as statistical validation tests and empirical Bayesian comparison, these methodologies have limitations in measuring the net effect of traffic safety policy by eliminating confounding factors. To investigate the net effect of traffic safety policy, this study proposed propensity score matching approaches to estimate causal treatment effects of empirical safe driving education by controlling confounding factors. The results revealed that drivers who received empirical safe driving education policy are less likely to cause traffic accidents than drivers who didn’t get such training by 16.4%. In particular, the treatment effect of education policy is more effective for older people than younger people, those in their seventies rated 36.7%, and those in their sixties are 28.5%. In addition, taxi drivers showed the highest treatment effect of education policy on reducing road traffic accidents by 39.2%.

Keywords

commercial vehicles

effect of traffic safety

probit regression

propensity score matching

traffic safety experience education

교통분야에서 교통안전에 대한 문제는 매우 중요한 이슈가 되었다. 1997년 스웨덴에서 시작한 비전제로 교통안전 정책이 알려진 이후로, 여러 국가에서 도로 교통안전 문제를 해결하기 위해 다양한 정책과 관련 기술이 채택되어지고 있다. 교통안전 정책의 실효성을 높이기 위해서는 이러한 정책과 처치가 교통사고 예방에 미치는 잠재적 영향에 대한 평가가 필요하다. 그러나 자료의 한계와 방법론적 문제로 인해 안전정책의 효과를 정량화 하는데 한계가 있다. 기존 연구들은 통계적 타당성 검증, 경험적 베이지안 방법 등을 주로 채택하였지만 이러한 방법론은 교란요인을 제거하여 교통안전정책의 순수 효과를 측정하는데 한계가 있다. 교통안전정책의 순수효과를 알아보기 위해 본 논문은 교란요인을 통제하는 성향점수매칭방법을 사용하여 교통안전체험교육 정책의 순수효과를 제시하였다. 분석결과 교통안전 체험교육을 이수한 운전자는 이수하지 않은 운전자에 비해서 교통사고 재발율이 16.4% 감소하는 것으로 나타났다. 연령별로 세분화하여 비교한 결과 70대가 36.7%, 60대가 28.5%로 고연령대가 저연령대에 비해 효과가 큰 것으로 나타났으며, 업종별로는 택시 업종이 39.2% 가장 효과가 높은 것으로 나타났다.

키워드

사업용자동차

교통안전체험교육

성향점수매칭

교통사고 감소효과

프로빗 회귀모형

References

Bonifaz J.L., Fasanando A. (2022), The Use of Propensity Score Matching Methodology for Analysis of Vonsession: The Case of Road Networks in Peru, Case Studies on Transport Policy, 10, 2350-2357. 10.1016/j.cstp.2022.10.017

Ding Hongliang, Sze N.N., Li Haojie, Guo Yanyong (2021), Affected Area and Residual Period of London Congestion Charging Scheme on Road Safety, Transport Policy, 100, 120-128. 10.1016/j.tranpol.2020.10.012

Elvik R. (1997), A Framework for Cost-Benefit Analysis of the Dutch Road Safety Plan, Institute of Transport Economics, Oslo, Norway.

Graham D.J., Naik C., Mc Coy E.J., Li H. (2019), Do Speed Cameras Reduce Road Traffic Collisions? PLoS One 14 (9), e0221267. 10.1371/journal.pone.022126731525209PMC6746359

Hauer E. (1992), Empirical Bayes Approach to Estimation of “unsafety”: The Multivariate Regression Method, Accid. Anal. Prev. 24(5), 457-477. 10.1016/0001-4575(92)90056-O

Hauer E., Harwood D.W., Council F.M., Griffith M.S. (2002), Estimating Safety by the Empirical Bayes Method: A Tutorial, Transp. Res. Rec. 1784, 126-131. 10.3141/1784-16

Jung E.B., Oh C. (2013), Methodology for Estimating Safety Benefits of Advanced Driver Assistant System, The Journal of The Korea Institute of Intelligent Transport System, 12(3), 65-77. 10.12815/kits.2013.12.3.65

Koo J.H., Jang J.Y., Choo S.H. (2017), Analysis of Traffic Accident Reduction Effect When Introducing Motorcycle Safety Inspection, J. Korean Soc. Transp., 35(1), Korean Society of Transportation, 25-36. 10.7470/jkst.2017.35.1.025

Lord D., Kuo P. (2012), Examining the Effects of Site Selection Criteria for Evaluating the Effectiveness of Traffic Safety Countermeasures, Accid. Anal. Prev. 47, 52-63. 10.1016/j.aap.2011.12.00822405239

Persaud B., Lyon C. (2007), Empirical Bayes before-after Safety Studies: Lessons Learned from Two Decades of Experience and Future Directions, Accid. Anal. Prev. 39, 546-555. 10.1016/j.aap.2006.09.00917081487

Rosenbaum P.R., Rubin D.B. (1983), The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika 70, 41-55. 10.1093/biomet/70.1.41

Rubin D.B. (2001), Using Propensity Scores to help Design Observational Studies: Application to the Tobacco Litigation, Health Services and Outcomes Research Methodology, 2(3), 169-188.

Sasidharan Lekshmi, Donnell Eric T. (2013), Application of Propensity Scores and Potential Outcomes to Estimate Effectiveness of Traffic Safety Countermeasures: Exploratory Analysis Using Intersection Lighting Data, Accident Analysis and Prevention, 50, 539-553. 10.1016/j.aap.2012.05.03622749318

Son S.O., Park J.Y., Kim M.W., Choe B.H. (2019), Assessing the Safety Effects of Reduction of Speed Limit on Urban Raods, J. Korean Soc. Transp., 37(6), Korean Society of Transportation, 514-524. 10.7470/jkst.2019.37.6.514

Talebian A., Zou B., Hansen M. (2018), Assessing the Impacts of State-supported Rail Services on Local Population and Employment: A California Case Study, Transport Policy, 63, 108-121. 10.1016/j.tranpol.2017.12.013

Wood Jonathan S., Donnel Eric T. (2016), Safety Evaluation of Continuous Green T Intersections: A Propensity Scores-Genetic Matching-Potential Outcomes Approach, Accident Analysis and Prevention, 93, 1-13. 10.1016/j.aap.2016.04.01527129112

Wood Jonathan S., Donnel Eric T., Porter Richard J. (2015), Comparison of Safety Effect Estimates Obtained from Emporocal Bayes Before-After Study, Propensity Scores-Potential Outcomes Framework, and Regression Model with Cross-Sectional Data, Accident Analysis and Prevention, 75,144-154. 10.1016/j.aap.2014.11.01925481539

Wood Jonathan S., Donnell Eric T. (2017), Causal Inference Framework for Generalizable Safety Effect Estimates, Accid. Anal. Prev. 104, 74-87. 10.1016/j.aap.2017.05.00128486151

Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 41
No :2
Pages :254-265
Received Date : 2022-12-12
Revised Date : 2023-02-16
Accepted Date : 2023-02-20
DOI :https://doi.org/10.7470/jkst.2023.41.2.254

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

All Issue