PURPOSES : This study aims to understand the characteristics of accidents involving autonomous vehicles and derive the causes of accidents from road spatial information through autonomous vehicle accident reports. METHODS : For this study, autonomous vehicle accident reports collected and managed by the CA DMV were used as data sources. In addition, spatial characteristics and geometric data for accident locations were extracted by Google maps. Based on the collected data, the study conducted general statistics, text embedding, and cross-analysis to understand the overall characteristics of autonomous vehicle accidents and their relationship with road spatial features. RESULTS : The analysis results for characteristics of autonomous vehicle accidents, applying statistical analysis and text embedding techniques, reveal that the damages caused by autonomous vehicle accidents are often minor, and approximately half of the accidents are triggered by other vehicles. It is noteworthy that accidents where autonomous vehicles are at fault are not uncommon, and when the cause of the accident is within the autonomous vehicle, the accident risk can increase. The accident analysis results using spatial data showed that the severity of accidents increases when on-street parking is present, when dedicated lanes for bicycles and buses exist, and when bus stops are present. CONCLUSIONS : Through this study, geometric and spatial elements that appear to have an impact on autonomous driving systems have been identified. The findings of this study are expected to serve as foundational data for improving the safety of autonomous vehicle operations in the future.

PURPOSES :With the increasing number of older drivers in an aging society, there is a growing need for research and planning on traffic safety for the older drivers using an improved road geometry design. This study also proposed a modified urban road interchange design, which aims to keep the older drivers away from accident-prone and high-traffic areas of the city.METHODS:In this study, we examined accident data records of older drivers to identify accident-prone zones and intersections; we studied the road geometry at these zones and analyzed if it was an underlying cause for higher number of accidents. Based on the research and subsequent analysis, we suggested plans for improvement of road geometry design at these intersections.RESULTS:By studying historic data and analyzing factors that affect the likelihood of accidents of vehicles driven by older drivers and after studying suitable traffic accident prediction models, we identified the major variables that need to be modified at accident-prone intersections, such as the width of a left turn lane at an intersection and the radius of the right turn lane at a street corner. The results have a significance probability of less than 0.001 and a 95% confidence level. To improve safety at the identified intersection, this study suggests the installation of a left-turn-lane-shaped Positive Offset and a right-turn-lane-shaped Slip Lane concept and an adjustment of intervals between intersections.

PURPOSES : Because elderly drivers are more prone to becoming confused when approaching an urban intersection and thus may yield prolong judgment and decision times than non-elderly drivers, to increase the comfort and safety of the intersection environment for elderly drivers, this study applied autonomous driving tests at an urban intersection to examine their driving characteristics. METHODS: To obtain a more comprehensive understanding of driving features, this study collected drive data of non-elderly drivers and elderly drivers via an autonomous experiment using OBD2 and an eye-tracker, in addition to performing a literature review on the measured visibility range of elderly drivers at intersections. This literature review was conducted considering the general knowledge of elderly drivers having relatively reduced visibility. Additionally, as they are commonly more vulnerable, this study analyzes characteristics of elderly drivers as compared to those of non-elderly drivers. CONCLUSIONS: The results of this study can be summarized as follows: 1) the peripheral visible distance of elderly drivers is reduced as compared to that of non-elderly drivers; 2) elderly drivers approach and proceed through intersections at slower speeds than non-elderly drivers; and 3) elderly drivers yield increased driving distances when performing a right or left turn as compared to non-elderly drivers as a result of their reduced speed and acceleration and larger turning radii relative to non-elderly drivers.

PURPOSES: The purpose of this study is to develop the U-turn accident model at 4-legged signalized intersections in urban areas. METHODS : In order to analyze the characteristics of the accidents which are associated with U-turn operation at 4-legged signalized intersections in urban areas and develop an U-turn accident model by regression analysis, the tests of overdispersion and zero-inflation are conducted about the dependent variables of number of accidents and EPDO (Equivalent Property Damage Only). RESULTS: As their results, the Poisson model fits best for number of accident and the ZIP (Zero Inflated Poisson) fits best for EPOD, the variables of conflict traffic, width of opposing road, traffic passing speed are adopted as independent variable for both models. The variables of number of bus berths and rate of U-turn signal time at which the U-turn is permitted are adopted as independent variable only for EPDO. CONCLUSIONS: These study results suggest that U-turn would be permitted at the intersection where the width of opposing road is wider than 11.9 meters, the passing vehicle speed is not high and U-turn operation is not hindered by the buses stopping at bus stops.

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

본 연구는 신호교차로 교통사고예측모형 구축 과정 중 일반적으로 제한된 변수의 선정 및 모형의 구축에만 주로 초점이 맞추어진 기존 방법론의 문제점을 개선하고, 자료조사 및 수집 과정에서 발생하는 자료의 불확실한 상태를 인정하면서 자료의 불확실성을 최소화하여 이용할 수 있는 방법론을 개발하는데 연구의 주안점을 두었다. 퍼지추론이론과 신경망이론을 이용한 모형을 구축하였고, 마지막으로 구축된 퍼지추론이론 모형 및 신경망이론 모형과 기존 회귀모형인 포아송 회귀모형간의 통계적인 검증과 실제 Data를 이용한 모형의 적정성을 검토하였다. 모형의 통계적인 검증시 기존모형에 비해 퍼지추론모형과 신경망이론모형이 더 설명력이 높은 것으로 나타났고, 검증에서도 퍼지추론이론과 신경망이론이 적절한 것으로 나타났으며 기존모형보다 사고건수를 예측하는 설명력이 높은 것으로 입증되었다. 본 연구에서 개발된 모형은 계획 및 운영단계에서 신호교차로의 안전성을 측정하는데 활용될 수 있으며, 궁극적으로는 신호교차로에서 교통사고를 줄이는데 기여할 수 있을 것으로 판단된다.

최근 녹색교통수단으로서 자전거의 이용자수는 급격하게 증가하고 있으나 차대 자전거사고 감소와 자전거 이용자의 안전성 향상에 대한 노력은 미비한 설정이다. 따라서 본 연구에서는 최적의 교차로 설계지침 제공 및 자전거 사고유형에 영향을 미치는 요인들을 면밀히 분석하여 교차로에서의 자전거사고 안전성 향상에 그 목적이 있다. 이를 위해 본 연구에서는 2005년도 인천광역시 사지교차로에서 발생한 56건의 자전거사고 자료에 대한 분석과 사고발생 교차로에 대한 현장조사를 실시하였으며, 다항로짓모형을 이용하여 3가지 경우에 대한 자전거 사고유형 분석모형을 개발하였다. 모형분석결과, 사망사고 유무, 부도로 교통섬 유무, 도로위계, 사고당시 날씨, 주도로 버스정류장 유무, 주도로 차로폭, 인적유발요인이 자전거 사고 유형에 중요한 변수로 나타났다.

1970년대 이후 급속한 경제성장과자동차의 증가로 인해 도심지의 극심한 교통정체와 환경파괴의 문제가 대두되었다. 이러한 도시의 부정적 문제를 해결하기 위해서는 승용차위주의 교통수단을 승용차외의 대체교통수단으로 전환하는 것이 보다 효과적인 방법이라 할 수 있다. 이러한 관점에서 자전거는 환경친화적인 그린교통수단(Green Mode)으로 세계 각국에서는 각광받고 있고, 국내에서도 자전거의 이용률을 높이기 위한 다양한 시도가 이루어지고 있다. 본 연구에서는 자전거 이용의 활성화를 위해 우선적으로 고려되어져야 하는 안전성 측면에서 자전거 사고에 영향을 미치는 영향인자들에 대한 분석을 시도하였다. 자전거 사고의 안전성 분석을 위하여 비선형 회귀분석을 통해 사고모델을 개발하였고, 이들 개발된 모델들을 이용하여 자전거사고에 영향을 미치는 주요설명변수들에 대한 분석을 시도하였다. 모델분석결과, 포아송회귀분석(poisson regression)이 모델개발에 가장 적합한 것으로 나타났으며, 자전거 사고에 영향을 미치는 변수로는 교통량, 진출입구 수, 지형, 자전거도로, 학교, 주거지역, 교차로의 크기 버스정류장 등으로 분석되었다.

최근 도시화에 의한 건축물 및 구조물의 대형화와 고층화에 따른 도시에서의 일반풍이 받는 영향이 복잡해졌다. 특히 건축물 및 구조물이 밀집한 시가지의 도로상에서는 도로의 방향에 따라서 풍향이 바뀌므로 풍향은 매우 복잡해진다. 풍향뿐만 아니라 건축물 및 구조물에 의해 국지적으로 풍속의 증가는 주변 건축물의 간판, 조명, 창문 등의 피해와 구조물의 형태 변형 등의 1차적인 피해가 나타난다. 또한 1차적인 물적 피해뿐만 아니라, 2차적으로 간판, 구조물 등의 추락으로 인하여 인명피해가 발생하고 있다. 이러한 피해가 발생하는 건물 및 구조물은 대부분 도로를 주변으로 형성되고 집중되어진다. 이에 본 연구에서는 도로의 형태 중 직선형태보다 바람의 집중과 분산으로 국지적인 풍속 증가가 나타날 것으로 예상되는 교차로를 중심으로 강풍 특성을 파악하고자 하였다. 본 연구를 통해 도심지 교차로 형태 중 갈래를 중심으로 나눠진 세갈래 T형, 세갈래 Y형, 네갈래 직각형, 기형(5갈래)에 대한 8개 풍향별 분석을 도심지 미기상 기후모델로 사용되고 있는 Envi-met 모델을 사용하여 도심지 교차로 형태에 따른 강풍 특성을 분석하였다.