This study evaluated the short- and long-term prediction performances of a transformer-based trajectory-forecasting model for urban intersections. While a previous study focused on developing the basic structure of a transformer model for future trajectory prediction, the present study aimed to determine a practical prediction sequence length. To this end, multiple transformer models were trained with output sequence lengths ranging from 1 s to 10 s, and their performances were compared. The trajectory data used for training were generated through a microscopic traffic simulation, and the model accuracy was assessed using the metrics average displacement error (ADE) and final displacement error (FDE). The results demonstrate that the prediction accuracy decreases significantly when the output trajectory length exceeds 3 s. Specifically, straight-driving trajectories exhibit rapidly increasing errors, while turning trajectories maintained a relatively stable accuracy. In contrast, for turning-driving trajectories, prediction errors increased sharply during short-term forecasting, but the increase was more gradual in long-term forecasts. Additionally, the long-term prediction models produced higher errors even in the initial 1-second outputs, implying a tendency toward conservative inference under uncertain future scenarios. This conservative behavior is likely influenced by the model’s effort to minimize the overall loss across a broader prediction window, especially when trained with Smooth L1 loss function. This study provides practical insights into model design for edge-computing environments and contributes to the development of reliable short-term trajectory prediction systems for urban ITS applications.

This study evaluated the safety impact of automated traffic enforcement cameras targeting tailgating behavior at signalized intersections by comparing traffic conditions shortly after installation and one year later. The Kukkiwon intersection in Gangnam-gu, Seoul, South Korea was selected as the study site. Individual vehicle speeds, accelerations, and subsequent distances were extracted from video data using YOLOv8 and ByteTrack, which are advanced deep learning-based object detection and tracking algorithms. Surrogate safety measures (SSM), such as time to collision (TTC), modified time to collision (MTTC), and proportion of stopping distance (PSD), were calculated to assess changes in traffic safety. Every SSM indicated an improvement one year after the installation of enforcement cameras, suggesting a reduction in collision risks. In particular, the PSD indicator showed a notable improvement, reflecting a better maintenance of safe following distances. These results highlight the effectiveness of automated enforcement in improving intersection safety and suggest its scalability to other intersections with similar tail-gating issues. Future research should explore the long-term and multisite effects using diverse intersection types and behavioral indicators.

Korea has many test beds where various mobility services are provided by automated vehicles. The test beds are operated in their operational design domain (ODD). However, disengagement frequently occurs, even in the ODDs of automated vehicles. In particular, human drivers have to take control of the automated vehicles at SAE Level 3 whenever the vehicles cannot drive by themselves because of an emergency or unknown factors. This study analyzed the driving safety of right turning at signalized intersections where automated vehicles face selfdriving issues because of potential conflicts with other vehicles, crossing pedestrians, and geometric factors. To conduct this analysis, we categorized right-turning intersections into two types with right-turning lanes and channelization islands and divided them into three sections, with a total of six sections. Subsequently, the six sections were compared with each other by disengagements of the automated vehicles as the key index to investigate their self-driving safety. Their significant differences indicate that ODD-related variables must be considered when designing and updating target test beds for automated vehicles.

미·중 기술패권 경쟁 가운데, 첨단 디지털 기술의 공급망과 데이터 수 집, 유출, 조작, 유포 등의 우려가 제기되면서 공급망안보와 데이터안보 가 교차하는 양상이 두드러지기 시작했다. 데이터안보나 공급망안보 각 각에 대한 기존 연구는 있었으나, 이 둘의 교차점에 초점을 맞춘 연구는 드물다. 이 연구에서 다루는 데이터안보 사안들은 정부뿐 아니라 개인이 나 민간 행위자들이 취급하는 데이터들이 국가안보적 사안으로 전환되는 모습을 보인다. 이러한 신흥위협이 공급망위협과 상호작용하며, 국가안보 적 우려로 비화한 사례로 화웨이의 5G, ZPMC 항만 대형 크레인, 미래 자동차의 핵심 기술 라이다, 중국산 DJI 드론 등이 있다. 본 연구는 이러 한 사례들에 대해 국가 차원에서 어떤 대응이 이루어졌는지 탐구하였다. 특히, 관련 이슈들을 안보화하고 대응책을 앞서 제시하고 있는 미국에 초점을 맞추었다. 또한 기존의 안보화 이론을 보완하여 정치경제적 측면 을 추가하였다. 행정명령, 전략서 발간, 연방 예산안 그리고 새로운 전문 기구들의 창설을 통해 신흥 위협에 대처하고 있으며, 그 과정에서 공급 망 재편, 표준, 규범, 규제의 설립을 통해 변화하는 국가의 안보 제공자 로서의 역할을 조명하였다.

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.

This study points out the traffic safety inhibition problem in which the yellow time length at a signal intersection can be set differently depending on the tram, and proposes a way to overcome it. This study proposes a twofold approach for trams to coordinate with the yellow signal phase designated for general vehicles, utilizing a schedule-speed-based operating speed adjustment and departure time control at stations. To validate the proposed method, simulations were conducted for a planned tram corridor in the Wirye District, Seoul. The simulation environment was developed using the simulation of the urban mobility model. The operational characteristics of tram services in the main and branch-line sections were analyzed. In the upstream section of the mainline, the operating speed was 22.04 km/h, with a total dwell time of 400 s across 10 stations, resulting in zero intersection stops. Regarding the downstream section, the operating speed was 17.71 km/h, and one intersection stop was observed. In contrast, for the upstream section of the branch line, the operating speed was 14.73 km/h and there were two intersection stops, while the downstream section yielded an operating speed of 18.12 km/h, and also comprised two intersection stops. This study examined a novel operational strategy for trams near intersections along tram corridors without directly modifying the signal system. This strategy effectively utilizes tram stop dwell time and traffic signal information. It focuses on adjusting tram speeds to ensure smooth and safe passage through signalized intersections and optimizing operations so that trams approach and cross intersections at a consistent speed. Designed as a universal model, this strategy is adaptable to various regions and environments, rather than being limited to a specific area. It is expected to support tram operation planning by considering diverse factors, such as urban structure, traffic density, and intersection characteristics. Moreover, it provides valuable guidance to ensure the punctuality and stability of tram operations in a range of urban and traffic contexts.

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.

긴급차량 골든타임 확보를 위해 지방자치단체에서는 ‘긴급차량 우선신호’ 시스템을 도입하고 있다. 그러나 긴급차량 우선신호로 인하 여 일반차량의 지체시간은 증가하게 되고 기존 신호주기로 바로 복귀하여 교차로 전체의 지체를 유발할 것으로 예상된다. 해당 지체 를 해결하기 위해 일반차량을 고려한 ‘회복신호 산정’ 연구가 수행되고 있다. 그러나 현재까지 교차로 유형별로 적절한 회복신호는 무 엇인지에 대한 연구는 부족한 상황이다. 본 연구에서는 교차로를 유형별로 구분하여 일반차량 지체도를 감소시키기 위한 회복신호의 필요성을 검증하고자 한다. 우선신호의 경우 긴급차량이 교차로를 통과하기까지 걸리는 시간을 계산하여 부여하였다. 회복신호의 경우 는 우선신호에서 부여한 시간만큼 일반차량에게 보상하는 신호방식을 적용하였으며, 도입 효과를 SUMO 교통 시뮬레이션을 통해 비 교 분석하였다.

PURPOSES : Even when autonomous vehicles are commercialized, a situation in which autonomous vehicles and regular drivers are mixed will persist for a considerable period of time until the percentage of autonomous vehicles on the road reaches 100%. To prepare for various situations that may occur in mixed traffic, this study aimed to understand the changes in traffic flow according to the percentage of autonomous vehicles in unsignalized intersections. METHODS : We collected road information and constructed a network using the VISSIM traffic simulation program. We then configured various scenarios according to the percentage of autonomous vehicles and traffic volume to understand the changes in the traffic flow in the mixed traffic by scenario. RESULTS : The results of the analysis showed that in all scenarios, the traffic flow on major roads changed negatively with the mix of autonomous vehicles; however, the increase or decrease was small. By contrast, the traffic flow on minor roads changed positively with a mix of autonomous vehicles. CONCLUSIONS : This study is significant because it proactively examines and designs traffic flow changes in congested traffic that may occur when autonomous vehicles are introduced.

Plastics are widely used in industries in human society and because of their structural stability, degradation is a serious global issue. To estimate the degradation of plastic, 31 edible mushrooms were cultured with the selected plastic films (polyethylene [PE], polystyrene [PS], and poly(ethylene terephthalate) [PET]) for 3 months at 25 °C. Measuring the weight of the films showed that four species of mushrooms, namely Porostereum spadiceum, Ganoderma lucidum, Coprinellus micaceus, and Pleurotus ostreatus, exhibited the highest degrees of plastic degradation. In addition, the mushrooms and fungi that exhibited the most significant plastic degradation were cross-cultured to promote this degradation. As a result, cross-cultivation of G. lucidum and Aspergillus niger showed a weight loss of 2.49% for the PET film. For the PS film, Aspergillus nidulans showed a weight loss of 4.06%. Cross-cultivation of A. nidulans and C. micaceus, which showed a weight loss of 2.95%, was noted as an alternative for PS biodegradation, but is harmful to humans. These bio-degradation effects of edible mushroom will contribute to the development of alternatives for eco-friendly plastic degradation.

PURPOSES : This study evaluates the effectiveness of traffic flow optimization when giving safety strategy guidance to a connected autonomous vehicle (CAV) based on information received through infrastructure cooperation in a V2X environment for non-signal intersection. METHODS : To evaluate the effectiveness of safety strategy guidance based on developed traffic flow control algorithm at a non-signalized intersection, it was implemented on simulation. A scenario based on the Level of Service (LOS) and the market penetration rate(MPR) of autonomous vehicles was established. The simulation results were divided into safety, operation, and environment to evaluate the effect, and the effect of optimizing traffic flow was finally derived through the integrated evaluation score. RESULTS : As a result, when safety strategy guidance was provided, the number of conflicts and CO emissions decreased by about 29% and about 15%, improving safety and environmental performance. In the case of operation, the mean of delay time was increased overall by 1%, but in the case of MPR 50 and above, the delay time was reduced by about 38%, thereby increasing operation. Finally, the aspect of traffic flow optimization, effectiveness of safety strategy guidance was derived through the integrated evaluation score, and the average integrated evaluation score improved from MPR 20 or higher. CONCLUSIONS : Providing guidance had the effect of optimizing traffic flow at a non-signal intersection. In the future, V2X communications will provide CAV with algorithm-based guidance developed in this study to control driving behavior. it will support safe and efficient driving at non-signal intersections.

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.

목적 : 본 연구는 웨스턴 온타리오 어깨 불안정 지수(Western Ontario Shoulder Instability Index: WOSI)를 한국어로 번역하고 측정 속성을 조사하고자 하였다. 연구방법 : 번역은 번역과 역번역 과정을 포함하는 다수의 문헌에서 사용되고 있는 가이드라인에 따라 진행되었다. 연구대 상자는 한국어판 WOSI (K-WOSI)와 Quick Disabilities of the Arm, Shoulder and Hand for Koreans (K-QuickDASH) 검사를 수행하였으며, K-WOSI는 일정한 시간이 지난 후에 같은 대상자에게 반복 측정되었다. 연구자 는 측정결과를 활용하여 내적일치도, 검사·재검사 신뢰도, 동시타당도, 바닥 및 천장 효과를 분석하였다. 결과 : 16명의 환자가 연구에 참여하였으며, 총점 분석 시 내적일치도(Cronbach’s alpha = 0.97)와 검사·재검사 신뢰 도(Pearson’s r = 0.86, Intraclass Correlation Coefficient = 0.92)가 통계학적으로 유의하였다. 평가의 하위 영역 간의 신뢰도 분석 시에도 통계학적으로 유의한 결과가 확인되었다. 또한 K-WOSI와 K-QuickDASH의 상관성이 높았 으며(Pearson’s r = 0.88), K-WOSI의 바닥 및 천장 효과가 발생하지 않았다. 결론 : 본 연구에서 K-WOSI가 어깨가 불안정한 환자들의 삶의 질을 평가하는데 있어 신뢰도와 타당도가 높은 평가로 확 인되었다.

PURPOSES : This paper proposes an artificial neural network (ANN)-based real-time traffic signal time design model using real-time field data available at intersections equipped with smart intersections. The proposed model generates suitable traffic signal timings for the next cycle, which are assumed to be near the optimal values based on a set of counted directional real-time traffic volumes. METHODS : A training dataset of optimal traffic signal timing data was prepared through the CORSIM Optimal Signal Timing program developed for this study to find the best signal timings, minimizing intersection control delays estimated with CORSIM and a heuristic searching method. The proposed traffic signal timing design model was developed using a training dataset and an ANN learning process. To determine the difference between the traditional pre-time model primarily used in practice and the proposed model, a comparison test was conducted with historical data obtained for a month at a specific intersection in Uiwang, Korea. RESULTS : The test results revealed that the proposed method could reduce control delays for most of the day compared to the existing methods, excluding the peak hour periods when control delays were similar. This is because existing methods focus only on peak times in practice. CONCLUSIONS : The results indicate that the proposed method enhances the performance of traffic signal systems because it rapidly provides alternatives for all-day cycle periods. This would also reduce the management cost (repeated field data collection) required to increase the performance to that level. A robust traffic-signal timing design model (e.g., ANN) is required to handle various combinations of directional demands.

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.

The interest in greenhouse gases (GHG) emitted from all industries is emerging as a very important issue worldwide. This is affecting not only the global warming, but also the environmentally friendly competitiveness of the industry. The fisheries sector is increasingly interested in greenhouse gas emissions also due to the Paris Climate Agreement in 2015. Korean industry and government are also making a number of effort to reduce greenhouse gas emissions so far, but the effort to reduce GHG in the fishery sector is insufficient compared to other fields. Especially, the investigation on the GHG emissions from Korean fisheries did not carry out extensively. The studies on GHG emissions from Korean fishery are most likely dealt with the GHG emissions by fishery classification so far. However, the forthcoming research related to GHG emissions from fisheries is needed to evaluate the GHG emission level by species to prepare the adoption of Environmental labels and declarations (ISO 14020). The purpose of this research is to investigate which degree of GHG emitted to produce the species (swimming crab and snow crab) from various fisheries. Here, we calculated the GHG emission to produce the species from the fisheries using the life cycle assessment (LCA) method. The system boundary and input parameters for each process level are defined for LCA analysis. The fuel use coefficients of the fisheries for the species are also calculated according to the fuel type. The GHG emissions from sea activities by the fisheries will be dealt with. Furthermore, the GHG emissions for producing the unit weight species and annual production are calculated by fishery classification. The results will be helpful to establish the carbon footprint of seafood in Korea.