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 : To prevent an increasing number of drowsiness-related accidents, considering driver fatigue is necessary, which is the main cause of drowsiness accidents. The purpose of this study is to propose a methodology for selecting drowsiness hotspots using continuous driving time, a variable that quantifies driver fatigue. METHODS : An analysis was conducted by dividing driver fatigue, which changes according to time and space, into temporal and spatiotemporal scenarios. The analysis technique derived four evaluation indicators (precision, recall, accuracy, and F1 score) using a random forest classification model that is effective for processing large amounts of data. RESULTS : Both the temporal and spatiotemporal scenarios performed better in models that reflected the characteristics of road sections with changes in time and space. Comparing the two scenarios, it was found that the spatiotemporal scenario showed a difference in precision of approximately 10% compared with the temporal scenarios. In addition, [Model 2-2] of the spatiotemporal scenario showed the best predictive power by assessing the model’s accuracy via a comparison of (1-recall) and precision. This shows better performance in predicting drowsy accidents by considering changes in time and space together rather than constructing only temporal changes. CONCLUSIONS : To classify hotspots of drowsiness, spatiotemporal factors must be considered. However, it is possible to develop a methodology with better performance if data on individuals driving vehicles can be collected.

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

PURPOSES : The primary objective of this study is to analyze the relationship between the factors that affect traffic incident duration in the mainline, tunnel, and ramp segments of an expressway. In addition, this study derived the most suitable statistical prediction model based on various incident duration distributions. METHODS : South Korean expressway crash data for 11 years, from 2011 to 2021, were analyzed. The incident durations on the mainline, tunnel, and ramp segments were selected using the accelerated failure time model, which is a parametric survival analysis approach. RESULTS : The mainline segment showed that the incident duration increased during accidents, including guard pipe collisions, multivehicle collisions, and snowfall. In particular, collisions in a tunnel with shoulder facilities increase the incident duration, while decreasing the time in the ramp segment. CONCLUSIONS : The incident duration model for each segment type yielded the most accurate results when applying a log-logistic distribution.

The preliminary study was conducted on the use of navigation instruments and programs conducted in the previous demonstration and experiment of the training ship SAEBADA, real-time sharing and accuracy of land and sea information, and the development of real-time effective information transmission and reception and management system programs. Based on this, this study used the training ship CHAMBADA, which is similar in length to the tonnage of the fishing boat. The purpose is to present errors in ship situation-specific information data, alarm systems for marine information, and land and ship transmission and reception programs collected using intelligent information management systems to find out whether they can be applied and used in fishing boats. It plans to conduct research on direct and indirect safety and ship’s stability when the intelligent information management system operates in real time in the operation of ships considering the characteristics of small fishing boats and the characteristics of fishing.

This study aims to understand the current status of exemptions for traffic accidents during the return of emergency vehicles and to provide suggestions for improvement. A survey was conducted on 3,500 firefighters to investigate the perception of traffic accidents during the return of emergency vehicles, and responses from 505 participants were analyzed. Based On the demographic characteristics and perception of the participants, frequency analysis and variance analysis were used as research methods to analyze basic statistics and the current situation. The results showed that firefighters have concerns and anxieties about traffic accidents during the return of emergency vehicles, and the need for applying exemptions and enacting explicit legal provisions was statistically confirmed. Based on these results, we suggest a policy for exemptions to improve the preparation for re-deployment and to alleviate the concerns and anxieties of firefighters.

This study would analyze a survey conducted with 609 workers to use the results as materials for the settlement of safety culture for the characteristics of the establishment by assessing the status of the workers’ exposure to harmful risks and the impact on safety culture to present the following conclusions. This study has significance in that it found that exposure to harmful/dangerous factors in the establishment and the workers’ experiences of occupational accidents affect their perception of safety culture and that in particular, ‘worker safety priority’ of the safety cultural factors was an important factor, and it is judged that continuous studies are necessary so that the perceptions of safety culture will spread in the establishment.

Ensuring the safe arrival of delivery cargo at its intended destination is of utmost importance. Truck drivers play a crucial role in guaranteeing the secure delivery of cargo without any mishaps. However, there are various factors that may lead to delayed arrival of trucks at their destination, such as late departures or prolonged loading operations. The timely departure of cargo transportation is contingent upon several variables, including the driver's experience, cargo volume, and loading time. If the transportation commencement is delayed, it may increase the risk of accidents due to an elevated operating speed. Consequently, we conducted a study to investigate the correlation between cargo loading time, cargo volume, driving experience, and the likelihood of accidents. Our findings indicate that both cargo volume and driver experience can impact the likelihood of vehicle accidents. Furthermore, all factors can have an interactive effect on the occurrence of accidents. However, extending the loading time may mitigate the impact on the likelihood of accidents.

The delivery truck is traveled around the delivery area for a long time. Truck drivers cause traffic accidents because of long hours of driving, fatigue, and speeding, etc. In this study, we will test the factors for preventing accidents of drivers. We would like to find factors that affect accidents and improve that can prevent accidents. As in the results of the study, accidents occur when traffic increases and profits increase among driver age, career, and profit factors. In addition, if the volume of traffic increases during the season, the number of accidents increases further. Therefore, in order to prevent truck accidents, a stable cargo quantity must be allocated to truck and cargo must be delivered.

Recently, the crisis of demographic extinction is rising in Korea more than any other country, and it is difficult for industrial sites to maintain without 'foreign workers'. Industrial accidents and accident deaths of foreign workers account for 7.6% and 12.3% of the total, through an in-depth analysis of fatal accidents in the manufacturing industry, differences and similarities between fatal accidents of foreign workers and all fatal accidents were confirmed in terms of occurrence type, workplace size, length of service, employment type, etc. In this study, customized countermeasures were found.

The Severe Disaster Punishment Act had recently been established in order to promote safety and health (OSH) management system for severe accident prevention. OSH management system is primarily designed based on risk assessments; however, companies in industries have been experiencing difficulties in hazard identification and selecting proper measures for risk assessments and accident prevention. This study intended to introduce an accident analysis method based on epidemiological model in finding hazard and preventive measures. The accident analysis method employed in this study was proposed by the U.S. Department of Energy. To demonstrate the effectiveness of the accident analysis method, this study applied it to two accident cases occurred in construction and manufacturing industries. The application process and results of this study can be utilized in improving OSH management system and preventing severe accidents.

PURPOSES : This study aims to conduct a sensitivity analysis to determine the major factors affecting traffic accidents involving elderly pedestrians. METHODS : In this study, a regression tree model was built based on a non-parametric statistical model using data on traffic accidents involving elderly pedestrians. Using this model, we analyzed the degree of change in the probability of pedestrian fatalities. RESULTS : Results of the model analysis show that the first major factor combination affecting traffic accidents involving elderly pedestrians is speeding, night time, and road markers. The second combination is night time and arterial roads (national and local highways). The last combination that may lead to such accidents is heavy vehicles and federally funded local highways. CONCLUSIONS : Preventive measures, such as speed control, proper lighting, median strips, designation of pedestrian protection zones, and guidance of detours, are necessary to manage high-risk combinations causing accidents of the elderly.