PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method.
METHODS: In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models.
RESULTS: The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with R2: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with R2: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model’s statistical significance.
CONCLUSIONS: Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

ABSTRACT
 1. 서론
  1.1. 연구의 배경 및 목적
  1.2. 연구의 범위 및 내용
 2. 이론적 배경
  2.1. 통계학적 방법을 이용한 교통사고모형 개발
  2.2. Generalized Regression Neural Network
 3. 교통사고모형 개발
  3.1. 데이터 수집 및 분석
  3.2. 통계학적 방법을 통한 교통사고모형 개발
  3.3. 머신러닝을 통한 교통사고모형 개발
 4. 머신러닝의 교통사고모형의 예측정확도분석
  4.1. 교통사고모형의 적합도 및 설명력 분석
  4.2. 교통사고모형의 유연성 분석
 5. 결론
 REFERENCES

• 이상혁(한국건설기술연구원 인프라안전연구본부) | Lee Sang Hyuk
• 우용한(경일대학교 도시인프라공학부) | Woo Yong Han 교신저자