본 연구는 목포대교 부근 해상교통 현황을 분석하고 선박의 안전한 교량 통항 방안을 검토하였다. AIS 데이터를 활용하여 교통 밀집도, 항적, 통과선, 교각과의 이격거리를 분석한 결과, 목포대교를 통과하는 선박은 설계지침보다 열악한 조건에서 항해하고 있었으며 교량 하부 수로 통항에 관한 명확한 규정이 부재한 것으로 나타났다. 항적 분석 결과, 선박 조종법과 외력, 타선박 존재 여부에 따라 항적 분포가 달라져 위험성이 확인되었으며, 교통 밀집도 분석에서는 좁은 항로폭과 정기선 운항으로 충돌 위험이 증가했다. 통과선 분석에서 는 내항, 주경간, 측경간, 외항 모두에서 항적이 중첩되어 충돌 확률이 높았고, 근접도 분석에서는 주탑 PY1에 최소 82m까지 근접 항해하 는 사례가 확인되어 통항 지원 시스템과 안전 대책 마련이 요구되었다.

This study aims to provide a basis for selecting the appropriate traffic-flow evaluation indicators by quantitatively analyzing the relative importance of such indicators in mixed traffic environments in which automated vehicles (AVs) and conventional vehicles coexist. As AV technology progresses and its adoption increases, establishing reliable evaluation criteria that accurately reflect the characteristics and performance of traffic systems under transitional conditions is crucial. Thus, approximately 40 domestic and international studies were reviewed in this study, from which 45 evaluation indicators were identified. These indicators were classified into three major categories: mobility, safety, and environment. Five frequently used and representative indicators were selected from each category based on the appearance frequency and relevance. An analytic hierarchy process survey was conducted with a group of transportation experts to derive the relative importance (weights) of both the major categories and individual indicators. The analysis revealed that safety (0.53676) was the most important category, followed by mobility (0.34795) and environment (0.11528). After combining the weights of the categories and sub-indicators, the top three indicators, i.e., time to collision (TTC), time exposed to TTC, and deceleration rate to avoid crashes, appeared to be safety related and associated directly with the collision risk. These findings suggest that, in the early stages of AV deployment, traffic evaluations should prioritize safety considerations over mobility or environmental factors to ensure the successful integration of AVs into existing traffic systems.

Traffic congestion and abrupt speed variations in tunnels increase crash risks and reduce traffic operational efficiency. Thus, a pacemaker system (PMS) was developed to stabilize traffic flow by guiding drivers to maintain uniform speeds through the use of sequentially illuminated LED lights installed along tunnel walls. This study aims to quantitatively evaluate the effects of a PMS on traffic operational efficiency and safety in the Geumnam Tunnel of the Seoul–Yangyang Expressway via a driving simulation. In speed-recovery scenarios, sequential LED lights effectively encouraged drivers to gradually restore their speed. Consequently, the average speed increased significantly, whereas both the difference in speed and the space-varying volatility of speed decreased, thus indicating enhanced driving consistency and improved flow stability. In speed-reduction scenarios, drivers’ deceleration responses were compared under three PMS operational types: flashing yellow, message display, and combined flashing with a message. Combined flashing with a message yielded the most controlled and pronounced deceleration, thus facilitating drivers in reducing their speed smoothly without abrupt braking or instability. The results collectively demonstrate that a PMS can serve a dual function by supporting both speed recovery under normal conditions and safe deceleration in accident cases. These findings provide empirical evidence of the effectiveness of a PMS as an intelligent tunnel-traffic management system and highlight its potential as a proactive safety technology. Furthermore, this study offers practical insights for future PMS designs as well as operational guidelines for enhancing traffic efficiency and driver safety in tunnel environments.

Conventional fixed-time traffic signal operations at urban intersections are typically based on prescheduled plans that presume stable and recurring traffic patterns, particularly during peak commuting hours. However, recent societal changes—including flexible work schedules, telecommuting, and evolving workweek structures—have introduced greater variability in traffic demand, thereby diminishing the effectiveness of traditional peak-hour-focused control strategies. This study investigated the performance of an AI-based adaptive traffic signal control system that operated independently of predefined time plans. A field demonstration was conducted in Jeju City, South Korea, where the system was deployed in both the cyclic and acyclic operation modes. By leveraging real-time traffic data obtained from AI-enabled video detectors, the system adjusted the signal timings on a per-second basis in response to dynamic traffic conditions. The performance was evaluated against the conventional time-of-day (TOD) control method under diverse traffic scenarios, including typical weekdays, weekends, and local event days. The AI-based system achieved substantial reductions in intersection delays—24.3% on weekdays, 22.2% on weekends, and 17.1% on event days—compared with the TOD baseline. Moreover, it preserved a comparable level of traffic progression (measured by the proportion of non-stop vehicle flows) even during acyclic operations. The greatest efficiency gains were observed during the nighttime and low-traffic periods, underscoring the capacity of the system to minimize unnecessary delays under variable conditions. These results highlighted the potential of AI-based adaptive signal control as a viable alternative to conventional fixed-time operations, offering enhanced responsiveness and operational flexibility in increasingly complex urban traffic environments. Future research will focus on scaling the system to larger networks and developing integrated optimization strategies across multiple intersections.

As conventional road traffic noise prediction models are designed to estimate long-term representative noise levels, capturing fine-scale noise fluctuations caused by real-world traffic dynamics is challenging. A previous study proposed a microscopic road traffic noise model (MTN) can calculate time-series noise levels with a resolution of 1 s using the concept of a moving noise source. In this study, two experiments were conducted to verify the accuracy of the noise prediction of the model. First, by comparing the calculated noise levels of two conventional road traffic noise models and the MTN in a simple road simulation environment, it was confirmed that the calculation error was within 3 dB(A) when calculating the 1-h equivalent noise level. Second, an experiment was conducted to verify the noise prediction error of the MTN on six actual roads. A comparison of the calculated noise level using the MTN based on traffic data collected from actual roads with the measured noise level on real roads showed that the calculated noise level achieved a mean absolute error (MAE) of 1.88 dB(A) from the equivalent noise level and 1.28 dB(A) from the maximum noise level. This was similar to the MAE of the foreign road traffic noise models. However, when the location of the receiver is within 10 m of the road, an error of more than 3 dB(A) occurs because of the simplicity of the MTN propagation model, which remains a problem that must be solved in the future. This study proved that the noise level calculation using the MTN is similar to the noise of an actual road environment. Additionally, the continuous development of the MTN is expected to make it an effective alternative for the management of road noise.

This study aims to evaluate traffic safety facilities in school zones in Busan Metropolitan City through Importance-Performance Analysis. This study investigated the traffic safety facilities in nine school zones, which have relatively more traffic accidents in Busan Metropolitan City from 2020 to 2022, through a field study and an Analytic Hierarchy Process(AHP). It identified their performance(i.e., compliance rate) and importance to derive measures for the improvement of traffic safety facilities in school zones. The field study showed that the compliance rate of starting points among traffic safety signs was low, and no speed limits were complied with the installation regulations among traffic road markings, but road safety facilities were generally well managed and operated. As a result of AHP, the order of importance was road safety facilities, traffic safety signs, and traffic road markings. More specifically, speed bumps, safety signs, and crosswalks were found to be more important than others in road safety facilities, traffic safety signs, and traffic road markings, respectively. Importance- Performance(compliance) Analysis revealed that the traffic safety facilities necessary to be most urgently improved are starting points. This result can be resorted to underlying measures to determine priorities for installing and operating traffic safety facilities in school zones.

PURPOSES : The reliability of traffic volume estimates based on location intelligence data (LID) is evaluated using various statistical techniques. There are several methods for determining statistical significance or relationships between different database sets. We propose a method that best represents the statistical difference between actual LID-based traffic volume estimates and the VDS values (i.e., true values) for the same road segment. METHODS : A total of 2,496 datasets aggregated for 1-h LID and VDS data were subjected to various statistical analyses to evaluate the consistency of the two datasets. The VDS data were defined as the true values for comparison. Four different statistical techniques (procrutes, 2-sample t-test, paired-sample t-test, and model performance rating scale) were applied. RESULTS : In cases where there is a specific pattern (e.g., traffic volume distribution considering peak and off-peak times), distribution tests such as Procrustes or Kolmogorov-Smirnov are useful because not only the prediction accuracy but also the similarity of the data distribution shape is important. CONCLUSIONS : The findings of this study provide important insight into the reliability of LID-based traffic volume estimation. To evaluate the reliability between the two groups, a paired-sample t-test was considered more appropriate than the performance evaluation measure of the machine-learning model. However, it is important to set the acceptance criteria necessary to statistically determine whether the difference between the two groups in the paired-sample t-test varies according to the given problem.

현재, 교통안전진단의 경우 차량 및 보행자의 교통사고를 미연에 방지하고 도로의 전체적인 안전을 도모하고자, 교통안전법 제34조 에 의거하여, 수행 조건에 부합한 경우 교통안전진단을 받도록 규정하고 있다. 교통안전진단의 경우 도로의 구분에 따라 다른 기준을 적용하고 있으며, 도로별 길이를 기준으로 수행 여부를 판단하고 있다. 교통안전진단의 경우 도로의 설계단계, 개시 전 단계 및 운영단계 등 3가지로 구분되어 수행되고 있으며, 각각의 단계별로 진단 수행 내용 및 범위가 조금씩 다르게 진행된다. 설계 단계에서의 교통안전진단의 경우, 해당 도로의 실시 설계 내용을 바탕으로 도로의 안전 을 판단하며, 개시 전 단계의 경우 도로의 신설 이후 운영 전 도로의 안전을 평가한다. 마지막으로 운영 단계의 교통안전진단의 경우 현재 운영 중인 도로에 대하여 도로의 안전을 평가하는 것이다. 본 연구에서는 진단단계별 교통안전진단 중 도로 설계단게에서 수행 시 발생될 수 있는 한계점을 파악하고, 이를 보완할 수 있는 방 안을 제시하여 그 효과를 분석하고자 한다. 또한, 국제 기준으로 운영되고 있는 iRAP(International Road Assessment Programme)의 SR4D( Star Rating for Design)을 통해 설계단계의 교통안전진단 수행 시 효과적이고 안전한 진단결과를 도출해내고자 한다.

Assessment of noise exposed population is to check the environment noise level and social influence in order to reduce the risks such as annoyance and disturbance that are generated by environmental noise. Also, this method suggests the preferential noise abatement policy and action plan by accurately finding the area that the noise causes harmful effect to human health. Recently, a noise map, which can predict noise in comprehensive area, is used for the assessment of noise exposed population, breaking from the methods using existing measures. In particular, countermeasure for noise can be considered more effectively by using assessment methods of noise exposed population for specific noise level, area and building types which are the main input factors in noise maps. In this study, we propose noise prediction at traffic noise due to noise map.

본 연구에서는 기존의 신호체계에서 발생하는 황색 신호 딜레마 상황에서 운전자의 상태를 파악하고 새로운 신호체계를 제안하고자 한다. 특히, 생체신호 분석을 통해 운전자 중심의 대처모형을 제안한다. 이를 위해 자동차 그래픽 시뮬레이터를 통해 교차로 도로 주행상황을 구현하여 기존의 신호체계와 본 연구에서 제안하는 신호체계에서 운전자의 생리적 반응을 관찰하여 규명하고자 한다. 따라서 대조군(기존 신호체계)과 새로운 황색 신호체계를 실험군(새로운 신호체계)으로 나누어 20대 초보 운전자를 중심으로 실험을 진행하였다. 그 결과, 대조군보다 실험군에서 교감신경의 출현이 우세하였으며 통계적으로 유의차가 인정되었다(p<0.05). 이를 통해 새로운 신호체계가 운전자가 긴장감을 유발하는 것처럼 보이지만 교감신경과 부교감신경의 비율이 6:4로 이상적인 균형으로 해석할 수 있다. 결론적으로, 본 연구에서 제안하는 대처 신호체계를 교통체계에 적용한다면 운전자가 더욱 안정적인 주행이 가능할 것으로 보인다.