This study aimed to investigate the factors affecting the severity of taxi traffic accidents at intersections in Busan and propose measures to improve traffic safety. This study collected data on taxi traffic accidents that occurred at intersections in the Metropolitan City of Busan during the past 3 years (2020–2022) from Traffic Accident Analysis System(TAAS) and road views, and analyzed factors affecting their severity by employing an ordered probit model. The severity of taxi traffic accidents worsened with violations of (among others) traffic signals and pedestrian protection during January, April, and September. In addition, when a major street was operated with a permissive left turn, the severity of taxi traffic accidents worsened. Measures to improve traffic safety suggested in this study included safety education that focused on particular violations for taxi drivers, mandatory education for transport employees in an experiential format, support of video storage devices for driving records, policy establishment for the promotion and certification of good and bad driving videos, time adjustment of joint safety management inspection, and left-turn signal operation with an unprotected system and P-turn guidance.

PURPOSES : According to government data, the Black Spot Program has resulted in an average 28.8% reduction in traffic accidents within one year of project implementation in areas where road conditions improved. However, there has been a lack of in-depth analysis of the midto- long-term effects, with a predominant focus on short-term results. This study aimed to analyze the mid-to-long-term effects of the Black Spot Program to assess the sustainability of its reported short-term impact. Additionally, the differences in the mid-to-long-term effects were investigated based on the scale of traffic accidents at intersections and the characteristics of these effects are revealed. METHODS : The mid-to-long-term effects of the Black Spot Program were analyzed at 122 intersections in Seoul, Korea, where the program was implemented between 2013 and 2017, using traffic accident data spanning five years before and after implementation. Additionally, the differences in the program's effects were analyzed at the top-100 intersections with the highest traffic accident concentration in Seoul using the chi-square test to identify these differences. To theoretically validate these differences, the Hurst exponent, commonly used in economics, was applied to analyze the regression to the mean of the intersections and reveal the correlation with improvement. RESULTS : Through the Black Spot Program at 122 intersections, a 33.3% short-term accident reduction was observed. However, the midto- long-term effect analysis showed a 25.8% reduction, indicating a slightly smaller effect than previously reported. Specifically, the top-100 intersections exhibit a 15.4% reduction. A chi-square test with a 95% confidence level indicated significant differences in the program’s effects based on the scale of traffic accidents at intersections. The Hurst index (H ) was measured for the top-100 intersections, yielding H = 0.331. This is stronger than the overall H = 0.382 for all intersections in Seoul, suggesting that the regression to the mean is more pronounced, which may lead to a lower effectiveness of the improvement. CONCLUSIONS : The mid-to-long-term effects of the Black Spot Program were relatively lower than its short-term effects, with larger differences in effectiveness observed based on the scale of traffic accidents at intersections. This indicates the need to redefine the criteria for selecting project targets by focusing on intensive improvements at intersections, where significant effects can be achieved.

PURPOSES : This study aimed to derive the factors that contribute to crash severity in mixed traffic situations and suggest policy implications for enhancing traffic safety related to these contributing factors. METHODS : California autonomous vehicle (AV) accident reports and Google Maps based on accident location were used to identify potential accident severity-contributing factors. A decision tree analysis was adopted to derive the crash severity analyses. The 24 candidate variables that affected crash severity were used as the decision tree input variables, with the output being the crash severity categorized as high, medium, and low. RESULTS : The crash severity contributing factor results showed that the number of lanes, speed limit, bus stop, AV traveling straight, AV turning left, rightmost dedicated lane, and nighttime conditions are variables that affect crash severity. In particular, the speed limit was found to be a factor that caused serious crashes, suggesting that the AV driving speed is closely related to crash severity. Therefore, a speed management strategy for mixed traffic situations is proposed to decrease crash severity and enhance traffic safety. CONCLUSIONS : This paper presents policy implications for reducing accidents caused by autonomous and manual vehicle interactions in terms of engineering, education, enforcement, and governance. The findings of this study are expected to serve as a basis for preparing preventive measures against AV-related accidents.

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 : The main purpose of this study is to identify directions for improvement of triangular islands installation warrants through analysis of the characteristics of crashes and severity with and without triangular islands on intersections. METHODS : The data was collected by referring to the literature and analyzed using statistical analysis tools. First, an independence test analyzed whether statistically significant differences existed between crashes depending on the installation of triangular islands. As a result of the analysis, individual prediction models were developed for cases with significant differences. In addition, each crash factor was derived by comparison with each model. RESULTS : Significant differences appeared in the "crash frequency of serious or fatal" and "crash severity" owing to the installation of triangular islands. As a result of comparing crash factors through the individual models, it was derived that the differences were dependent on the installation of the triangular islands. CONCLUSIONS : As a result of reviewing previous studies, it is found that improving the installation warrants of triangular islands is reasonable. Through this study, the need to consider the volume and composition ratio of right-turn vehicles when installing a triangular island was also derived; these results also need to be referred to when improving the triangular island installation warrants.

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.

PURPOSES: This study aimed to analyze traffic accidents at circular intersections, and discuss accident reduction strategies based on land use and vehicle type. METHODS : Traffic accident data from 2010 to 2014 were collected from the “traffic accident analysis system”(TAAS) data set of the Road Traffic Authority. To develop the accident rate model, a multiple linear regression model was used. Explanatory variables such as geometry and traffic volume were used to develop the models. RESULTS: The main results of the study are as follows. First, it was found that the null hypotheses that land use and vehicle type do not affect the accident rate should be rejected. Second, 16 accident rate models, which are statistically significant (with high R2 values), were developed. Finally, the area of the central island, number of speed humps, entry lane width, circulatory roadway width, bus stops, and pedestrian crossings were analyzed to determine their effect on accidents according to the type of land use and vehicle. CONCLUSIONS: Through the developed accident rate models, it was revealed that the accident factors at circular intersections changed depending on land use and vehicle type. Thus, selecting the appropriate location of bus stops for trucks, widening entry lanes for cars, and installing splitter islands and optimal lighting for motorcycles were determined to be important for reducing the accident rate. Additionally, the evaluation showed that commercial and mixed land use had a weaker effect on accidents than residential land use.

PURPOSES: The goal of this study is the development of roundabout accident models for urban and non-urban areas. METHODS: This study performed a comparative analysis of the regional factors affecting accidents. Traffic accident data were collected for the period 2010~2014 from the TAAS data set of the Road Traffic Authority. To develop the roundabout accident models, the Poisson and negative binomial regression models were used. A total of 25 explanatory variables such as geometry, and traffic volume were used. RESULTS : The key findings are as follows: First, it was found that the null hypotheses that the number of accidents is the same should be rejected. Second, three Poisson regression accident models, which are statistically significant (p2 of 0.154 and 0.385) were developed. Third, it was noted that although the common variable of the three models (models Ⅰ~Ⅲ) is the number of entry lanes, the specific variables are entry lane width, roundabout sign, number of circulatory roadways, splitter island, number of exit lanes, exit lane width, number of approach roads, and truck apron. CONCLUSIONS: The results of this study can provide suggestive countermeasures for decreasing the number of roundabout accidents.

PURPOSES : The purpose of this study is to develop models of accidents occurring at circular intersections related to the time of day and night and driver gender, and to provide countermeasures for safer circular intersections. METHODS: Seventy intersections built before 2008 were surveyed for inclusion in the modeling. Traffic accident data from 2008 to 2014 were collected from the TAAS data set of the Road Traffic Authority. Sixteen variables explaining the accidents including geometry and traffic volume were selected from the literature and seven multiple linear regression models were developed using SPSS 20.0. RESULTS: First, the null hypotheses, that the number of traffic accidents are not related to driver gender or time of day, were rejected at a 5% level of significance. Second, seven statistically significant accident models with R2 value of 0.643-0.890 were developed. Third, in daytime models by gender, when the right-turn-only lane was selected as the common variable, the number of lanes, presence of driveways and speed humps, diagrammatic exit destination sign, and total entering traffic volume were evaluated as specific variables. Finally, in nighttime models by gender, when the diagrammatic exit destination sign was selected as the common variable, total entering traffic volume, presence of right-turnonly lanes, number of circulatory road way lanes, and presence of splitter islands and driveways were identified as specific variables. CONCLUSIONS: This study developed seven accident models and analyzed the common and specific variables by time of day and gender. The results suggest approaches to providing countermeasures for safer circular intersections.

PURPOSES :This study deals with traffic accidents involving trucks. The objective of this study is to develop a traffic accident model for trucks at roundabouts.METHODS :To achieve its objective, this study gives particular attention to develop appropriate models using Poisson and negative binomial regression models. Traffic accident data from 2007 to 2014 were collected from TAAS data set of road traffic authority. Thirteen explanatory variables such as geometry and traffic volume were used.RESULTS :The main results can be summarized as follows: (1) two statistically significant Poisson models (ρ2 = 0.398 and 0.435) were developed, and (2) the analysis revealed the common variables to be traffic volume, number of exit lanes, speed breakers, and truck apron width.CONCLUSIONS :Our modeling reveals that increasing the number of speed breakers and speed limit signs, and widening the truck apron width are important for reducing the number of truck accidents at roundabouts.

OBJECTIVES : The objective of this study is to develop a traffic accident model of a roundabout based on the type of land use. METHODS : The traffic accident data from 2010 to 2014 were collected from the“ traffic accident analysis system (TAAS)”data set of the Road Traffic Authority. A multiple linear regression model was utilized in this study to analyze the accidents based on the type of land use. Variables such as geometry and traffic volume were used to develop the accident models based on the type of land use. RESULTS : The main results are as follows. First, the null hypothesis that the type of land use does not affect the number of accidents is rejected. Second, four accident models based on the type of land use have been developed, which are statistically significant (high R2 values). Finally, the total entering and circulating volumes, area of the central island, number of speed breakers, mean number of entry lanes, diameter of the inscribed circle, mean width of the entry lane, area of the roundabout, bus stops, and number of circulatory roadways are analyzed to see how they affect the accident for each type of land use. CONCLUSIONS: The development of the accident models based on the type of land use has revealed that the accident factors at a roundabout are different for each case. Thus, more speed breakers in commercial areas and an inscribed circle of proper diameter in commercial and residential areas are determined to be important for reducing the number of accidents. Additionally, expanding the width of the entry lanes, decreasing the area of the roundabouts in residential areas, and reducing the conflict factors such as bus stops in green spaces are determined to be important.

PURPOSES: There are many recently constructed roundabouts in Jeollabuk-do province. This study analyzed how roundabouts reduce the risk of accidents and improve safety in the province. METHODS: This study analyzed safety improvement at roundabouts by using an accident prediction model that uses an Empirical Bayes method based on negative binomial distribution. RESULTS : The results of our analysis model showed that the total number of accidents decreased from 130 to 51. Roundabouts also decreased casualties; the number of casualties decreased from 7 to 0 and the seriously wounded from 87 to 16. The effectiveness of accident reduction as analyzed by the accident prediction model with the Empirical Bayes method was 60%. CONCLUSIONS : The construction of roundabouts can bring about a reduction in the number of accidents and casualties, and make intersections safer.

회전교차로는 일반적인 평면교차로보다 상충횟수가 적고 진입속도가 느려 교통사고 발생건수와 피해정도 가 작다는 특징을 가지고 있으며, 이는 회전교차로 관련 각종 사고 자료나 연구들로 증명되고 있다. 이에 정 부에서는 회전교차로의 도입활성화 방안 추진에 따라 회전교차로의 설치를 증가시키고 있는 추세이다. 하지만, 회전교차로에서도 기하구조, 운전자의 부주의 등으로 교통사고가 지속적으로 발생하고 있으며 측면충돌, 후미추돌, 기타 등 다양한 충돌유형이 존재한다. 또한 오늘날 우리나라는 영업용 택배차량의 증가에 따라 화물차 등록대수가 지속적으로 증가하고 있는 추세이다. 화물차의 경우 무거운 화물을 싣고 있고, 차량자체도 크기 때문에 사고가 일어나면 대형사고로 이어질 가능성이 크다. 그러나 기존 회전교차 로 사고와 관련된 연구들 대부분이 전체차량에 대한 연구이며, 화물차에 관한 연구는 부족한 실정이다. 따라서 이 연구에서는 국내 회전교차로에서의 화물차 충돌유형을 측면충돌, 후미추돌, 기타로 나누어 모형을 개발하고 충돌유형별로 어떠한 요인이 사고에 영향을 미치는지 비교분석하는데 목적이 있다. 모형의 종속변수는 국내 회전교차로 76개소에서 발생한 2007~2014년까지 8개년도 화물차 교통사고건 수이며, 독립변수로는 교통류특성을 나타내는 교통량과 기하구조요인 등을 적용하였다. 이를 위해 통계프 로그램 LIMDEP 8.0과, SPSS20.0을 이용하였으며, 모형은 가산자료 모형인 포아송과 음이항 회귀모형 중 추정에 의해 더 적합한 모형을 선정하였다. 모형추정결과 충돌유형별 사고모형의 공통변수는 화물차 턱 폭이 채택되었으며, 특정변수로 측면충돌사고는 차로분리섬 유무, 화물차 턱 유무, 후미추돌사고는 유 입차로 폭, 제한속도표지판 유무, 기타사고는 내접원 직경, 교통량이 채택되었다. 또한 우도비 검정을 수 행한 결과 통계적으로 설명력이 높은 모형이 개발된 것을 알 수 있었다.

회전교차로의 교통사고에 영향을 미치는 요인들은 매우 다양하며 복합적으로 작용한다. 교통사고에 영 향을 미치는 독립변수는 크게 인적･환경적･물리적 요인으로 구분된다. 이 연구에서는 다양한 요인들 중 특히 토지이용에 중점을 두어 교통사고와의 관계를 분석하고자한다. 토지이용과 교통은 매우 밀접한 관계를 가지고 있으나, 이와 관련하여 토지이용과 교통사고의 관계를 분 석한 연구는 많지 않다. 용도에 따라 다르지만, 토지이용은 교통수요를 유발하므로 교통사고 분석에 있어 서 토지이용에 대한 고려가 필요할 것으로 보인다. 회전교차로 주변의 토지이용 특성에 따라, 교통량이나 대형차 비율 등 통행의 양상이 다르게 나타나기 때문에 교통사고 발생에 영향을 미치는 정도도 다를 것이 다. 따라서 이 연구는 ʻ회전교차로 주변의 토지이용이 회전교차로 내 교통사고 발생에 차이를 보인다.ʼ는 가설을 전제로, 토지이용별 회전교차로 사고모형을 구축하는 데에 그 목적이 있다. 분석 대상으로는 국내 회전교차로 100개소를 선정하였다. 종속변수는 TAAS에서 수집한 2007~2014년 에 발생한 교통사고 건수이며, 독립변수로는 교통사고에 영향을 미칠 것으로 예측되는 교통량, 기하구조, 환경 및 운전자 요인 등의 변수를 선정하였다. 분석에 앞서 먼저 회전교차로 주변의 용도지역(주거지역, 상업지역, 자연녹지지역)을 4가지 유형으로 분류하였다. 토지이용의 분석 범위는 회전교차로 중심으로부 터 250m내에서 가로망을 따라 형성된 토지용도를 기준으로 하였다. 유형 Ⅰ은 상업용도의 비율이 높은 지역이 해당되며, 유형 Ⅱ는 상업지역과 주거지역이 혼재된 지역, 유형 Ⅲ은 주거지역의 비율이 높은 지 역, 그리고 유형 Ⅳ는 자연녹지지역으로 이루어진 지역이 해당된다. 따라서 분류된 토지이용 유형별로 SPSS 20.0을 활용하여 다중선형회귀모형을 이용하여 사고모형을 구축하고자한다. 개발된 모형을 통해 토지이용 유형별 교통사고 발생 요인을 파악할 수 있어, 보다 효율적으로 회전교차 로에서의 안정성 향상을 위한 대안을 수립할 수 있을 것으로 기대된다.