PURPOSES : This study empirically analyzes the determinants of fatal accidents based on raw data on traffic accidents occurring in Chungnam in 2020.
METHODS : Regression models based on theoretical arguments for fatal traffic accidents are estimated using a binomial logit model.
RESULTS : The prediction model for fatal accidents is affected by the degree of urbanization of the region, month and day of the accident, type of accident, and type of law violation. In addition, speeding or illegal U-turns among law violations appear more likely to result in fatal accidents. The road surface conditions at the time of the accident do not show a significant difference in the probability of fatality among traffic accidents. However, the probability of a fatal accident is rather lower in case of a snowy road; this is plausible, as drivers tend to drive more carefully in bad weather conditions.
CONCLUSIONS : Among traffic accidents, fatal accidents appear to be affected by the time and place of the accident, type of accident, and weather conditions at the time of the accident. These analysis results suggest policy implications for reducing fatal accidents and can be used as a basis for establishing related policies.
PURPOSES : Traffic congestion on freeway generally occurs when the traffic volume exceeds the road capacity. Most traffic manuals *such as the Korean Highway Capacity Manual) present the highway capacity as approximately 2,000 units/hour. However, in the real world, freeway congestion occurs for various reasons, including unusual driver behaviors, physical road limitations, and large traffic volumes. Thus, the flow rate at a traffic breakdown can have a wide range of volumes. Therefore, using 5-min volume and speed data from the field, this study explores the stochastic features of traffic breakdowns on major urban freeways in Seoul.
METHODS : First, a breakdown point is defined by applying a wavelet transform to identify the sharp drop in the speed data near freeway bottlenecks. Second, based on the flow rate at and before a breakpoint, a survival analysis is performed to construct the probability distributions of the traffic breakdown. Log-rank tests are also conducted to verify the similarities of the distributions between freeways.
RESULTS : The analysis results confirm the stochastic features of the urban freeways in Seoul. Specifically, the freeways have typical S-shaped distributions of breakdown probabilities. However, the distributions rise steeply (exceeding a 50% of breakdown probability) at flow rates of 1,150 vphpl to 1,700 vphpl; this is lower than the general expectation.
CONCLUSIONS : The statistical differences in the probability distributions for freeways indicates that applying a general standard to every urban highway could raise problems. This study has a limitation in identifying the specific causes of traffic congestion owing to the by physical relationships between individual vehicles. An investigation if vehicle trajectory data should be conducted to examine these aspects in further detail.
해상교통분석은 복잡해지는 해양환경에 따라 발생하는 문제해결을 위해 다방면으로 시행되고 있다. 하지만 4차 산업혁명으로 부터 도래된 자율운항선박 개발 등의 해사분야 동향은 해상교통분석에도 변화가 필요함을 암시한다. 이에 해상교통분석의 개선점을 식별 하고자 관련 연구를 분석하였으며, AIS데이터의 활용도가 높은 반면에 해도정보의 활용은 그 중요도에 비해 부족한 것으로 조사되었다. 이에 본 연구는 자율운항선박의 상용화에 대비한 해상교통분석의 개선점으로서 수치해도 데이터와 선박운항데이터인 AIS데이터를 복합 적으로 활용하는 방법을 제시하였다. 연구결과로써 해상교통분석에 수치해도데이터를 활용하였을 때 추출 가능한 해상교통특성을 제시 하였으며 이는 향후 자율운항선박의 도입을 위한 해상교통분석에 활용가능할 것으로 기대된다.
해양사고 예방을 위해서는 사고의 원인과 결과에 대한 분석 및 진단뿐만 아니라, 사고의 발생 패턴과 변화 추이를 예측함으로 써 정량적 위험도를 제시할 필요성이 있다. 선박교통과 관련된 해양사고 예측은 선박의 충돌위험도 분석 및 항해 경로 탐색 등 선박교통 의 흐름에 관한 연구가 주로 수행되었으며, 해양사고의 발생 패턴에 대한 분석은 전통적인 통계 분석에 따라 제시되었다. 본 연구에서는 해양사고 통계 자료 중 선박교통관련 사고의 월별, 시간대별 발생 현황 데이터를 활용하여 해양사고 발생 예측 모델을 제시하고자 한다. 국내 해양사고 발생 현황 중 월별, 시간대별 데이터 집계가 가능한 1998년부터 2021년까지의 통계자료 중 선박교통 관련 데이터를 분류하 여 정형 시계열 데이터로 변환하였으며, 대표적인 인공지능 모델인 순환 신경망 기반 장단기 기억 신경망을 통하여 예측 모델을 구축하 였다. 검증데이터를 통하여 모델의 성능을 검증한 결과 RMSE는 초기 신경망 모델에서 월별 52.5471, 시간대별 126.5893으로 나타났으며, 관측값으로 신경망 모델을 업데이트한 결과 RMSE는 월별 31.3680, 시간대별 36.3967로 개선되었다. 본 연구에서 제안한 신경망 모델을 기 반으로 다양한 해양사고의 특징 데이터를 학습하여 해양사고 발생 패턴을 예측할 수 있을 것이다. 향후 해양사고 발생 위험의 정량적 제 시와 지역기반의 위험지도 개발 등에 관한 추가 연구가 필요하다.
PURPOSES : A highway operates in a continuous flow and has restricted access. When an accident occurs on a highway, the impact on the traffic flow is large. In particular, an accident that occurs in a tunnel has a more significant impact than an accident that occurs in a general section. Accordingly, the management agency classifies the tunnel as a dangerous section and manages a tunnel of more than 1000 m using the Tunnel Transportation Management System. The purpose of this study was to select dangerous tunnels that require intensive management for the efficient management of highway tunnels.
METHODS : In this study, for the selection of dangerous tunnels for expressways, all highway tunnels were classified into five clusters by characteristics. The traffic accident severity — equivalent property damage only (EPDO) — for each tunnel cluster was derived through a traffic accident analysis. Based on the severity analysis results, the safety performance function (SPF) for each cluster was established, and the accident risk tunnel was selected based on the potential safety improvement (PSI) value of each tunnel calculated using the empirical Bayes (EB) method for each tunnel cluster.
RESULTS : As a result of the analysis, accident risk tunnels were selected based on the PSI values of the tunnels for each highway tunnel group. Finally, 55 hazardous tunnels were identified as hazardous tunnels: 13 tunnels in Cluster 1, 3 tunnels in Cluster 2, 15 tunnels in Cluster 3, 18 tunnels in Cluster 4, and 6 tunnels in Cluster 5.
CONCLUSIONS : After classifying all 1232 tunnels on the highway into five clusters according to tunnel characteristics, EPDO analysis was performed for each tunnel cluster. To this end, the SPF for each cluster was constructed, and accident risk tunnels were selected based on the PSI value of each tunnel calculated using the EB method for each tunnel cluster. The tunnel cluster was classified as a typical tunnel type. As a result, most of the first and second values were calculated from cluster E (long tunnel cluster).
PURPOSES : In this study, the factors affecting the severity of traffic accidents in highway tunnel sections were analyzed. The main lines of the highway and tunnel sections were compared, and factors affecting the severity of accidents were derived for each tunnel section, such as the tunnel access zone and tunnel inner zone.
METHODS : An ordered probit model (OPM) was employed to estimate the factors affecting accident severity. The accident grade, which indicates the severity of highway traffic accidents, was set as the dependent variable. In addition, human, environmental, road condition, accident, and tunnel factors were collected and set as independent variables of the model. Marginal effects were examined to analyze how the derived influential factors affected the severity of each accident.
RESULTS : As a result of the OPM analysis, accident factors were found to be influential in increasing the seriousness of the accident in all sections. Environmental factors, road conditions, and accident factors were identified as the main influential factors in the tunnel access zone. In contrast, accident and tunnel factors in the tunnel inner zone were found to be the influencing factors. In particular, it was found that serious accidents (A, B) occurred in all sections when a rollover accident occurred.
CONCLUSIONS : This study confirmed that the influencing factors and the probability of accident occurrence differed between the tunnel access zone and inner zone. Most importantly, when the vehicle was overturned after the accident occurred, the results of the influencing factors were different. Therefore, the results can be used as a reference for establishing safety management strategies for tunnels or underground roads.
선박교통관제사는 선박 교통의 안전과 효율을 도모하기 위하여 선박교통관제 설비로 지정된 시스템 및 센서 장비를 활용하여 선박교통관제 업무를 수행한다. 선박교통관제의 효과적 운영을 위한 관제 필요 정보는 지정된 선박교통관제 설비 이외의 부가적인 정보 창구의 접근이 요구되며, 이러한 다양한 정보 열람 창구를 일원화하기 위하여 선박교통관제 클라우드 시스템 개발이 진행 중이다. 본 연구 에서는 선박교통관제 클라우드 시스템 도입에 따른 사용자 필요 정보 식별을 위하여 선박교통관제 업무 분석 및 운영 정보와 선박교통관 제 설비 연계 분석을 수행하였다. 국내외 문헌 검토와 전문가 인터뷰를 통하여 선박교통관제 업무 분석을 수행하였으며, 선박교통관제 설 비에 따른 필요 정보를 식별·연계하였다. 분석 결과 전체 내·외부 정보 창구의 필요 정보는 37개의 범주로 식별되었으며, 필수 및 보조관제 설비 이외 열람이 필요한 추가 요구 정보는 8개의 정보창구를 통하여 수집 가능한 것으로 확인되었다. 본 연구를 통하여 식별한 사용자 요 구사항은 선박교통관제 클라우드 시스템 구축을 위한 데이터 수집·처리 구조 설계에 적용될 것이다. 향후 시나리오 기반 관제 시스템 사용 자 운영 분석을 통하여 사용자 요구 및 필요 정보를 개정·보완하고, 시스템 인터페이스 디자인 설계에 관한 추가 연구가 필요하다.
PURPOSES : The logistic roads for freight transport along to the new port of Busan have been suffered by the rapid weather changes including high temperature and torrential rain. As a result, the roads require annual repair, which have been distressed seriously by the heavy logistic and environmental loads. Therefore, we need to identify the cause of the road pavement distresses and find a proper design method to minimize the pavement distress in order to prohibit the problem aggravated.
METHODS : The damaged conditions of the logistic roads were investigated on-site. In addition, applied pavement designs, real traffic volumes, and historical climatic information were intensively collected for this project. With the investigated and collected data Korean pavement design program (KPRP) was implemented to analyzed the causes of the damaged roads and conceive the pavement design draft optimized for the roads.
RESULTS : According to the investigation and KPRP analysis, the traffic volume to transport freights impacts significantly the pavement distress, so that a higher PG grade binder type should be used, for which polymer modified asphalt (PMA) binders are recommended. Moreover, its pavement thickness should be increased to secure load bearing capacity, but thickening the pavement has been discouraged due to difficulties induced by the road-sectional change, especially road-height change.
CONCLUSIONS : In conclusion, 5cm PMA overlay is suggested for the normal-scale maintenance, and 7cm PMA overlay for large-scale maintenance. Besides these, the application of Polymer-modified Stone Matrix Asphalt (PSMA) using PG76-22 binder would be the best preventive maintenance method, which has been well know as having higher fatigue resistant performance than general PMA. However, if we use PSMA, quality control should be very cautious since PSMA can be very susceptible premature distress if its production and construction are improperly proceeded.