PURPOSES : Driving simulations are widely used for safety assessment because they can minimize the time and cost associated with collecting driving behavior data compared to real-world road environments. Simulator-based driving behavior data do not necessarily represent the actual driving behavior data. An evaluation must be performed to determine whether driving simulations accurately reflect road safety conditions. The main objective of this study was to establish a methodology for assessing whether simulation-based driving behavior data represent real-world safety characteristics. METHODS : A 500-m spatial window size and a 100-m moving size were used to aggregate and match the driving behavior indicators and crash data. A correlation analysis was performed to identify statistically significant indicators among the various evaluation metrics correlated with crash frequency on the road. A set of driving behavior evaluation indicators highly correlated with crash frequency was used as inputs for the negative binomial and decision tree models. Negative binomial model results revealed the indicators used to estimate the number of predicted crashes. The decision-tree model results prioritized the driving behavior indicators used to classify high-risk road segments. RESULTS : The indicators derived from the negative binomial model analysis were the standard deviation of the peak-to-peak jerk and the time-varying volatility of the yaw rate. Their importance was ranked first and fifth, respectively, using the proposed decision tree model. Each indicator has a significant importance among all indicators, suggesting that certain indicators can accurately reflect actual road safety. CONCLUSIONS : The proposed indicators are expected to enhance the reliability of driving-simulator-based road safety evaluations.

ABSTRACT
1. 서론
2. 관련문헌 고찰
    2.1. 주행 시뮬레이션 기반 실제 도로 안전성 평가 연구
    2.2. 통계기법 및 머신러닝 기반 실제 도로 안전성 평가연구
    2.3. 본 연구의 의의
3. 분석 데이터셋 구축
    3.1. 주행 시뮬레이션을 통한 주행행태 데이터 수집
    3.2. 사고자료 수집
    3.3. 평가지표 선정
    3.4. 주행데이터 및 사고자료 분석구간 정의
    3.5. 실도로 및 주행 시뮬레이션 간 주행속도 비교
4. 연구 방법론
    4.1. 상관분석
    4.2. 음이항 모형 분석
    4.3. 의사결정나무 모형 분석
5. 분석 결과
    5.1. 상관분석 결과
    5.2. 음이항 모형 분석 결과
    5.3. 의사결정나무 모형 분석 결과
    5.4. 대표 평가지표와 안전성 간의 관계
6. 결론
감사의 글
REFERENCES

• 정아람(한양대학교 스마트시티공학과 석박사통합과정) | JUNG Aram
• 박현진(한국도로공사 도로교통연구원 책임연구원) | PARK Hyunjin
• 박동혁(한양대학교 교통, 물류공학과 석박사통합과정) | PARK Donghyeok
• 박준영(한양대학교 교통, 물류공학과 및 스마트시티공학과 부교수) | PARK Juneyoung
• 오철(한양대학교 교통, 물류공학과 및 스마트시티공학과 교수) | OH Cheol Corresponding author