In this study, we aim to classify personal mobility (PM)-related traffic crash data into four categories: PM-to-vehicle, PM-to-pedestrian, PM-single, and vehicle-to-PM crashes, and analyze the factors influencing the severity of each crash type. To overcome the limitations of existing studies in explaining the impact of independent variables on ordinal dependent variables, a random forest model was combined with the Shapley additive explanation technique. This approach visualizes the influence of independent variables on a dependent variable, providing clearer insights and enhancing interpretability. The analysis of PM traffic accidents, categorized into at-fault, single-vehicle, and victim accidents, revealed distinct key factors for each type. The main contributors to the severity of crashes caused by PM are traffic violations by teenagers and collisions with elderly pedestrians. Single-vehicle accidents were predominantly caused by overturn incidents, with inadequate driving skills among PM users aged 40 years and older, and significantly increasing severity. Victim accidents primarily occur at intersections, where the behavior of the at-fault driver and age of the PM user are critical factors influencing the severity. We identified various factors influencing the severity of PM crashes by type, highlighting the need for tailored policy measures. Proposed policies include physically separating bicycle–pedestrian shared spaces and strictly regulating illegal PM sidewalk riding, introducing PM licenses for teenagers to ensure compliance with traffic rules, and implementing regular safety education programs for all age groups. Although this study applied a new analytical technique, it relied on limited crash data, thus limiting the results to estimates.

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 : This study aimed to investigate the factors affecting the severity of traffic crashes caused by personal mobility (PM) devices compared with those involving victims. METHODS : Traffic crashes involving PM devices were used to build a non-parametric statistical model using a classification tree. Based on the results, the factors influencing both at-fault and victim-related crashes caused by PM devices were analyzed. The factors affecting accident severity were also compared. RESULTS : Common factors affecting the severity of traffic crashes involving both perpetrators and victims using PM devices include occurrences at intersections, crosswalks at intersections, single roads, and inside tunnels. Traffic law violations by PM device users (perpetrators) influence the severity of crashes. Meanwhile, factors such as the behavior of perpetrators using other modes of transportation, rear-end collisions, road geometry, and weather conditions affect the severity of crashes where PM device users are the victims. CONCLUSIONS : To reduce the severity of traffic crashes involving PM devices, it is essential to extend the length of physically separated shared paths for cyclists and pedestrians, actively enforce laws to prevent violations by PM device users, and provide systematic and regular educational programs to ensure safe driving practices among PM device users.

PURPOSES : As the number of fixed traffic enforcement equipments increase rapidly, it is necessary to improve efficient operation and management plans. The aim of this study is to evaluate the factors influencing fixed traffic enforcement equipment failure. METHODS : This study utilized binary logistic regression analysis using the database provided by the Korean Road Traffic Authority to evaluate the factors affecting the failure of fixed traffic enforcement equipment. RESULTS : As a result of the evaluation of this study, the main factors affecting failure were red-light camera, old equipment, Jeju, National expressways, and equipment with low speed limits. CONCLUSIONS : This study can be used as basic data on the improvement of operation management plas for maintenance of traffic enforcement equipment. Through this study, it will be possible to establish a step-by-step plan with high efficiency comapared to the input of required manpower.

PURPOSES : This study aims to identify the thresholds at which various factors affecting traffic crashes lead to actual traffic crashes METHODS : To verify the thresholds, we created scenarios and ran simulations with a combination of factors that affect traffic crashes. Lateral offset and minimum TTC were used to evaluate whether an incident occurred. RESULTS : In the first scenario, the most significant factor affecting traffic crashes is curvature, and it was found that the smaller the curvature(200 meters or less), the greater the deviation from the lane. And in the second scenario, especially the passenger car scenario, no accidents occurred when the curvature was greater than 90 meters and the speed was 40 km/h or less. The smaller the curvature and the higher the speed, the more accidents occurred. Similarly, in the bus scenario, no accidents occurred when the curvature was 120 meters or more and the speed was 30 km/h or less. Also, accidents tended to occur when the curvature was smaller and the speed was higher. CONCLUSIONS : Through this study, we derived the thresholds of factors that influence traffic crashes. The results are expected to help design and operate roads in the future and contribute to reducing traffic crashes.

PURPOSES : This study aims to analyze the causes of pedestrian traffic accidents on community roads. METHODS : This study collected variables affecting pedestrian traffic accidents on community roads based on field surveys and analyzed them using negative binomial regression and zero-inflated negative binomial regression models. RESULTS : Model analysis results showed that the negative binomial regression model is more suitable than the zero-inflation negative binomial regression model. Additionally, the segment length (m), pedestrian volume (persons/15 min), traffic volume (numbers/15 min.), the extent of illegal parking, pedestrian-vehicle conflict ratio, and one-way traffic (one: residential, two: commercial) were found to influence pedestrian traffic accidents on community roads. Model fitness indicators, comparing actual values with predicted values, showed an MPB of 1.54, MAD of 2.57, and RMSE of 7.03. CONCLUSIONS : This study quantified the factors contributing to pedestrian traffic accidents on community roads by considering both static and dynamic elements. Instead of uniformly implementing measures, such as pedestrian priority zones and facility improvements on community roads, developing diverse strategies that consider various dynamic factors should be considered.

PURPOSES : This study investigates the factors affecting extra-long tunnel accidents by integrating data on tunnel geometry, traffic flow, and traffic accidents and derives the underlying implications to mitigate the severity of accidents. METHODS : Two processes centered on three key data points (tunnel geometry, traffic flow, and traffic accidents) were used in this study. The first is to analyze the spatial characteristics of extra-long tunnel traffic accidents and categorize them from multiple perspectives. The other was to investigate the factors affecting extra-long tunnel traffic accidents using the equivalent property-damage-only (EPDO) of individual accidents and the aforementioned data as the dependent and independent variables, respectively, by employing an ordered logistic regression model. RESULTS : Gyeonggi-do, Gyeongsangnam-do, and Gangwon-do are three metropolitan municipalities that have a significant number of extra-long tunnel accidents; Busan and Seoul have the most extra-long tunnel accidents, accounting for 23.2% (422 accidents) and 18.6% (339 accidents) of the 1,821 accidents that occurred from 2007 to 2020, respectively. In addition, approximately 70% of extra-long tunnel traffic accidents occurred along tunnels with lengths of less than 2 km, and Seoul and Busan accounted for over 60% of the top 20 extra-long tunnels with accidents. Most importantly, the Hwangryeong (down) tunnel in Busan experienced the most extra-long tunnel traffic accidents, with 77 accidents occurring during the same period. As a result of the ordered logistic regression modeling with EPDO and multiple independent variables, the significant factors affecting the severity of extra-long tunnel traffic accidents were determined to be road type (freeway, local route, and metropolitan city road), traffic flow (speed), accident time (year, summer, weekend, and afternoon), accident type (rear end), traffic law violations (safe distance violation and center line violation), and offending vehicles (van, sedan, and truck). CONCLUSIONS : Based on these results, the following measures and implications for mitigating the severity of extra-long tunnel traffic accidents must be considered: upgrading the emergency response level of all road types to that of freeways and actively promoting techniques for regulating high-speed vehicles approaching and traversing within extra-long tunnels are necessary. In addition, the emergency response and preparation system should be reinforced, particularly when the damage from extra-long tunnel traffic accidents is more serious, such as during the summer, weekends, and afternoons. Finally, traffic law violations such as safe distance and centerline violations in extra-long tunnels should be prohibited.

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 : 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.

PURPOSES : In this study, we quantitatively prove the rubber necking phenomenon for highway traffic accidents and develop a calculation model based on the influencing factors. METHODS : Vehicle detector speed data in the opposite direction to the accident point were used based on the accident data on highways over the past three years, and a comparative verification was performed between nearby vehicle detector data to verify the reliability of the data. Accordingly, a binomial logistic model, ordinal probit regression model, and multilinear regression model were developed to compare the orientation. RESULTS : There was a difference in the influencing factors based on the dependent variable, and the day of the week, vehicle type, weather, longitudinal slope, and median height had an effect. Through a regression analysis, an influence coefficient was derived to calculate the driving speed deceleration value by rubbernecking. The results of the model analysis proved that the speed reduction caused by rubbernecking was more evident during the daytime than at night, during weekends compared to weekdays, and the speed reduction was more obvious for heavy vehicles compared to other types of vehicles. It can also be concluded that longer clearance time, higher accident severity, and higher traffic volume affect traffic delay. To verify the data and model equation, the mean prediction bias (MPB) and mean absolute deviation (MAD) were calculated for hundred cases randomly extracted from the collected accident data. These results were excellent. CONCLUSIONS : It can be developed into a human-engineered model that reflects various road/facility conditions, such as highways, other lanes, general roads, and roads without a median strip. This study is meaningful as a basic study on the quantitative effect of rubber necking.

PURPOSES : In this study, the factors influencing traffic accidents of commercial vehicles in the transportation industry were examined. The evidence observed in this study showed how it contributes to the establishment of safety management policies. METHODS : Safety management data obtained from the Korean Transportation Safety Authority were integrated. A multi-regression analysis was performed by comparing data for the past three years of traffic accident data. RESULTS : Through multi-regression analysis, items that significantly influenced the safety evaluation index (the number of traffic accidents per vehicle owned and the number of dangerous driving actions per 100 km per person) were analyzed. Items with similar patterns observed for various commercial vehicle industries were also analyzed. Monthly average drop off rate, number of law violations per driver, percentage of drivers above 65 years old, percentage of drivers that have completed the compulsory education, number of vehicles owned, and driving distance per vehicle are the variables that influenced the safety evaluation index. CONCLUSIONS : There is a growing need to establish safety management policies and management measures to enhance the voluntary safety management capabilities of the industry. Safety management should be conducted through the analysis of traffic accident impact factors for commercial vehicles.

해양사고에 관한 많은 연구와 분석에 따르면 약 80%가 인적 오류에 의하여 발생되고 있는 것으로 파악되고 있다. 해양사고의 예방대책 수립을 수립하기 위하여 사고를 일으킨 배후 인적 요인을 파악하는 연구가 반드시 필요하다. 따라서 본 연구의 주목적은 m-SHEL 모델을 이용하여 해상교통 관련 사고의 배후 인적 요인을 파악하고 분석하는 것이다. 다른 분야에서 사용되는 m-SHEL 모델은 일반적인 인적 요인의 개념을 기반으로 되어 있기 때문에 본 연구에서는 선박운항시스템에 수용하기 위하여 이 모델을 확장하여 인적 요인 을 정의하였다. 또한, 이 확장된 모델의 타당성을 SPSSWIN의 신뢰성 분석을 통하여 검증하였다. 그리고 이 확장된 m-SHEL 모델의 분류표 사용하여 해양안전심판원의 재결서에서 추출한 자료로부터 해상교통 관련 사고의 배후 인적 요인을 분석하였다. 해상교통 관련 사고의 배후 인적 요인을 분석한 결과 조선자 자신에 관한 요인 L이 가장 많았으며 다음으로 L-E, L-m, L-H, L-S 및 L-L 순으로 나타났다. 이 연구는 해상교통 관련 사고의 예방 및 해상안전관리시스템 구축을 위한 유용한 분석 결과를 제시함으로써 인적 요인에 의한 해상교통 관련 사고 방지에 기여할 것으로 판단된다.

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 objective of this study is to analyze factors affecting traffic accident severity for determining countermeasures on freeway climbing lanes. METHODS : In this study, an ordered probit model, which is a widely used discrete choice model for categorizing crash severity, was employed. RESULTS: Results suggest that factors affecting traffic accident severity on climbing lanes include speed, drowsy driving, grade of uphill 3%, gender (male offender and male victim), and cloud weather. CONCLUSIONS : Several countermeasures are proposed for improving traffic safety on freeway climbing lanes based on the analysis of crash severity. More extensive analysis with a larger data set and various modeling techniques are required for generalizing the results.

PURPOSES: This study analyzes the characteristics of traffic flow on freeway sections under daytime and nighttime conditions to improve traffic safety, and suggests a method to estimate an accurate freeway capacity value that reflects these characteristics. METHODS : The trends and differences in capacity were investigated using comprehensive field data collected under both daytime and nighttime conditions on freeway sections with designated speeds of 80, 100, and 120 kph. RESULTS : The capacity values under nighttime driving conditions were reduced by 3.3%, 6.9%, and 8.8% at 80, 100, and 120 kph, respectively. Several nighttime adjustment factors were deduced for each designated speed category from the analysis results. CONCLUSIONS : It is expected that more accurate capacity values can be estimated for freeway sections under nighttime conditions by applying the resulting adjustment factors. In addition, traffic safety will be improved through the increased efficiency of traffic management on these freeway sections.