PURPOSES : In this study, the main factors affecting the severity of traffic accidents among elderly drivers were reviewed, and accident factors with a high accident risk were analyzed. This provided basic data for preparing a traffic safety system for elderly drivers and establishing policies. METHODS : Based on machine learning, the major factors influencing accident severity (from the analysis of traffic accident data for elderly drivers) were analyzed and compared with existing statistical analysis results. The machine learning algorithm used the Scikit-learn library and Python 3.8. A hyperparameter optimization process was performed to improve the safety and accuracy of the model. To establish the optimal state of the model, the hyperparameters were set (K = 5) using K-fold cross-validation. The hyperparameter search applied the most widely utilized grid search method, and the performance evaluation derived the optimal hyperparameter value using neutral squared error indicators. RESULTS : The traffic laws, road sections of traffic accidents, and time zones of accidents were analyzed for accidents involving elderly drivers in Daejeon Metropolitan City, and the importance of the variables was examined. For the analysis, a linear regression model, machine learning-based decision tree, and random forest model were used, and based on the root mean square error, the random forest accuracy performance was found to be the best. Ultimately, 18 variables were analyzed, including traffic violations, accident time zones, and road types. The variables influencing the accident severity were the speed, signal violation, intersection section, late-night driving, and pedestrian protection violation, with the relative importance of the variables in the order of speed (0.3490966), signal violation (0.285967), and late-night driving (0.173108). These can be seen as variables related to the expansion of life damage owing to physical aging and reduced judgment abilities arising from decreases in cognitive function. CONCLUSIONS : Restricting the driving of the elderly on the expressway and at night is reasonable, but specific standards for driving restrictions should be prepared based on individual driving capabilities.

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

본 연구는 교통사고의 발생 유형과 교통사고 심각도(Severity)와의 관계를 규명함으로써 위험유형을 제시하고, 운전자 특성과 교통사고의 관계를 규명하고자 하였다. 교통사고 유형을 여덟 가지로 세분하고, 결과의 객관성 확보를 위해 안전벨트 착용여부를 추가하여 상해정도와의 관계를 분석하였으며, 위험그룹의 분류를 위한 운전자의 특성은 성별, 차종, 연령 등을 대상으로 하였다. 카테고리 자료의 분석을 위하여 로그-선형 모형 및 로짓 모형을 사용하였다. 분석결과 사고유형과 심각도와의 관계에서는 정면충돌 사고와 앞지르기시, 우회전시 사고가 부상 또는 사망사고에 연루될 가능성이 높았다. 위험그룹 분석에서는 20세 미만의 이륜차 운전자, 41세에서 50세까지의 택시 운전자가 가장 위험한 집단으로 분석되었으며 또한 남자보다는 여자가 승용차와 중형화물 등에 관계되었을 때 더 위험한 것으로 분석되었다. 따라서 교통사고 발생시 인명 피해를 줄이기 위해서는 정면충돌 사고와 앞지르기시, 우회전시 발생하는 사고를 줄일 수 있는 방안이 연구되어야 하고, 교통사고 취약계층으로 분석된 위험그룹에 대한 교통안전 교육 및 단속이 강화되어야 할 것이다.