PURPOSES: The purpose of this study is to develop a crash prediction model at signalized intersections, which can capture the randomness and uncertainty of traffic accident forecasting in order to provide more precise results. METHODS: The authors propose a random parameter (RP) approach to overcome the limitation of the Count model that cannot consider the heterogeneity of the assigned locations or road sections. For the model’s development, 55 intersections located in the Daejeon metropolitan area were selected as the scope of the study, and panel data such as the number of crashes, traffic volume, and intersection geometry at each intersection were collected for the analysis. RESULTS: Based on the results of the RP negative binomial crash prediction model developed in this study, it was found that the independent variables such as the log form of average annual traffic volume, presence or absence of left-turn lanes on major roads, presence or absence of right-turn lanes on minor roads, and the number of crosswalks were statistically significant random parameters, and this showed that the variables have a heterogeneous influence on individual intersections. CONCLUSIONS : It was found that the RP model had a better fit to the data than the fixed parameters (FP) model since the RP model reflects the heterogeneity of the individual observations and captures the inconsistent and biased effects.

ABSTRACT
 1. 서론
 2. 교통사고 모형 방법론
  2.1. 가산 모형(Count Model)
  2.2. Random Parameters 모형
 3. 모형 구축
  3.1. 자료 구축 및 분석
  3.2. 모형 구축 및 결과 분석
 4. 결론 및 향후 연구과제
 REFERENCES

• 박민호(인천발전연구원 교통물류연구실) | Park, Minho
• 홍정열(서울시립대학교 교통공학과) | Hong, Jungyeol Corresponding Author