본 연구에서는 Odds Ratio를 활용하여 야간 교통사고 위험성을 진단한 선행연구의 진단방법을 개선하였다. Östen Johansson 외(2009)는 Odds Ratio에서 착안한 야간 교통사고 위험성 진단방법을 제시하였는데, 이 방법의 가장 큰 장점은 교통량 데이터가 필요하지 않는 것이었다. Östen Johansson 외(2009)가 정의한 야간 교통사고 위험성은 {(Case Hour가 밤일 때 교통사고 발생건수)/(Case Hour가 낮일 때 교통사고 발생건수)}/{Case Hour가 밤인 날 Comparison Hour의 교통사고 발생건수}/(Case Hour가 낮인 날 Comparison Hour의 교통사고 발생건수)}이다. 이 때, Case Hour는 계절에 따라 낮인 날도 있고, 밤인 날도 있는 시간대이고, Comparison Hour은 연중 항상 밝은 시간대이다. Östen Johansson 외(2009)의 연구 결과, 야간에는 교통사고 위험성이 더 증가하는 것을 확인할 수 있었는데, 지역별로는 도시지역에서는 약 30% 교통사고 위험성이 증가했고, 지방지역에서는 약 50% 교통사고 위험성이 증가했다. 그러나 Östen Johansson 외(2009)의 야간 교통사고 위험성 진단방법은 교통량에 대한 고려가 없기 때문에, 연간 교통량이 일정해야만 사용할 수 있는 기법이다. 즉, 계절에 따라 교통량이 변화하는 경우에는 적용할 수 없는 방식이다. 따라서 본 연구에서는 Odds Ratio의 정의를 되짚고, 교통량을 고려하였을 때의 Odds Ratio를 활용한 야간 교통사고 위험성 진단방법을 제시하였다.

PURPOSES : Low visibility caused by dark surroundings at nighttime affects the likelihood of accidents, and various efforts, such as installing road safety facilities, have been made to reduce accidents at night. Despite these efforts, the nighttime severity index (SI) in Korea was higher than the daytime SI during 2011-2014. This study determined the factors affecting daytime and nighttime accident severity through a discriminant analysis. METHODS: Discriminant analysis. RESULTS: First, drowsiness, lack of attention, and lighting facilities affected both daytime and nighttime accident severity. Accidents were found to be caused by a low ability to recognize the driving conditions and a low obstacle avoidance capability. Second, road conditions and speeding affected only the daytime accident severity. Third, failure to maintain a safe distance significantly affected daytime accident severity and nonsignificantly affected nighttime accident severity. The majority of such accidents were caused by rear-end collisions of vehicles driving in the same direction; given the low relative speed difference in such cases, the shock imparted by the accidents was minimal. CONCLUSIONS: Accidents caused by a failure to maintain a safe distance has lower severity than do accidents caused by other factors.

PURPOSES : The purposes of this study are to compare the day and night characteristics and to develop the models of traffic accidents. in Rural Signalized Intersections METHODS : To develop day and night traffic accident models using the Negative Binomial Model, which was constructed for 156 signalized intersections of rural areas, through field investigations and casualty data from the National Police Agency. RESULTS : Among a total of 17 variances, the daytime traffic accident estimate models identified a total of 9 influence factors of traffic accidents. In the case of nighttime traffic accident models, 11 influence factors of traffic accidents were identified. CONCLUSIONS: By comparing the two models, it was determined that the number of main roads was an independent factor for daytime accidents. For nighttime accidents, several factors were independently involved, including the number of entrances to sub-roads, whether left turn lanes existed in major roads, the distances of pedestrian crossings to main roads and sub-roads, lighting facilities, and others. It was apparent that if the same situation arises, the probability of an accident occurring at night is higher than during the day because the speed of travel through intersections in rural areas is somewhat higher at night than during the day.

PURPOSES: This study suggests the application of glow line marking technology for reducing traffic accidents at nighttime. METHODS: In this study, using a statistical analysis, we analyzed the characteristics of traffic accidents occurring at nighttime. Next, the strength, weakness, opportunity, and threat (SWOT) factors were derived based on a current-status analysis of glow line marking technology and road environments. An SO strategy, ST strategy, WO strategy, and WT strategy were established in accordance with the four SWOT factors. RESULTS : This study suggests that the following strategies should be promoted to apply glow line marking technology to a road environment: 1) an activation strategy for the technological development of glow line markings for a new paradigm in reducing traffic accidents, 2) a benefit enhancement strategy applying glow line marking technology in places where nighttime traffic accidents frequently occur, 3) a strategy for the expansion of glow line marking by replacing streetlights, and 4) a strategy for enhancing road applications through the development of various line marking methods in consideration of both performance and costs. CONCLUSIONS : The application of glow line markings in a road environment can contribute to a reduction of traffic accidents at nighttime, and aid energy savings from the replacement of streetlights.