본 연구는 도로변 완충녹지에서 녹지구조와 공기 중 미세먼지 농도(지상 1.5m 지점)를 조사하여 미세먼지 농도에 영향을 주는 완충녹지의 구조적 요인을 파악하고자 하였다. 총 10개소의 완충녹지에서 측정된 미세먼지 농도를 도로측 미세먼 지 농도와 비교한 상대농도지수(Relative PM10 Index: RPM10)를 산출하였다. RPM10이 양의 값이면 녹지의 농도가 더 낮고, 음의 값이면 녹지의 농도가 더 높음을 의미한다. 미세먼지 농도 측정 결과 도로측과 비교하여 녹지측의 미세먼지 농도가 전반적으로 높았다. 녹지구조 변수와 상관분석 결과 RPM10은 관목층 피도, 전체 수목 피도, ㎡당 관목층 체적과 양의 상관성이 유의하였고, 평균 수고, 지하고, 식재 밀도, 교목층의 피도와 ㎡당 체적 등은 유의한 상관성을 확인할 수 없었다. 또한 완충녹지 배후공간이 막혀있는 곳의 RPM10이 비어있는 곳보다 낮은 차이가 인정되었 다. 회귀분석 결과 완충녹지 배후공간이 막힌 곳에서는 빈 곳보다 RPM10이 낮아지고(B=-.123, P=.000), 관목층 체적 (B=.674, P=.004)과 수목 지하고(B=.072, P=.015)가 증가하면 RPM10이 높아지는 것으로 파악되었다. 완충녹지에서 측정된 더 높은 미세먼지 농도는 녹지에서 미세먼지가 차단·정체된 결과로 판단된다. 정체된 미세먼지는 녹지 내부에서 제거되어 외부 미세먼지 농도 감소에 기여한다. 분석결과 관목층 식재 및 지하고가 낮은 교목·아교목층 수목의 배치는 미세먼지의 흐름 차단에 유리한 것으로 나타났다. 이러한 결과는 향후 도로변 완충녹지 조성뿐만 아니라 도시공원·녹지 계획의 수립 시 미세먼지 저감을 위한 식재 전략으로 고려될 수 있다.

도로변 투명 방음벽은 소음 저감 효과뿐만 아니라 경관 개선이라는 관점에서 널리 설치되고 있으나, 높은 반사율과 투명도로 인해 야생조류 충돌의 주요 원인으로 지목되고 있다. 본 연구는 경상남도 전역을 대상으로 조류 충돌 현황을 파악하고, 충돌 위험지역의 공간적 특성을 규명하여 투명 방음벽의 조류충돌 저감 대책 수립을 위한 기초 자료로 제공하고자 수행되었다. 2000년부터 2023년까지 현장 조사, 비영리 단체 자원봉사, 시민과학 자료를 통해 총 3,638건(74 종 3,686개체)의 충돌 정보를 수집하였다. 분석 결과 멧비둘기, 물까치, 직박구리 등 텃새가 주요 충돌 종으로 나타났으며, 계절별로는 겨울철이 가장 많았다. 방음벽 주변 토지피복은 충돌 여부와 무관하게 80m 이내 구간에서 시가화 건조지역과 초지 비율이 급격히 증가하는 경향을 보였다. 종분포 모델(MaxEnt) 분석 결과, 다양한 요인 중 산림과의 거리가 가장 큰 연관성을 보였으며, 산림-농업, 산림-도시 등 생태계 경계부의 저지대에서 충돌 위험이 집중되는 것으로 나타났다 (AUC=0.921). 이 결과는 투명 방음벽의 충돌 위험이 방음벽의 구조적 특징뿐만 아니라 주변 공간구조와 지형적 특성과 밀접하게 연관됨을 시사한다. 따라서 신규 방음벽 설치 시 방음벽의 조류충돌 저감 설계 반영과 함께, 시민과학 참여를 통한 지속적 모니터링 등을 통한 위험지역 도출과 고위험지역에 대한 우선 저감 조치가 필요하다.

In this study, we comparatively analyzed the efficiency of conventional image recognition methods and propose a digital information provisioning method for autonomous vehicle traffic safety facility recognition. We evaluated the practicality of both approaches from the perspective of autonomous vehicles' capabilities of processing regulatory information and the distribution of legal responsibility. Comprehensive field experiments were conducted at 9 major intersections in the Pangyo Techno Valley area of Hwaseong City over a 10- day period from July 12-23, 2021. Three test vehicles equipped with in-vehicle terminals and video cameras collected data through 300 driving scenarios, including 240 during peak hours and 60 during off-peak periods. The proposed digital information provision method exhibited superior performance, achieving a 100.0 % recognition success rate across all test scenarios and road conditions. In contrast, the conventional image recognition method exhibited significant variability in performance, ranging from 56.9 % in underpass conditions to 95.9 % in areas with communication interference, with an overall average of 70.8 %. The digital information provision method demonstrated superior performance compared to conventional image recognition approaches for autonomous vehicle regulatory compliance. The proposed approach delivered consistent and reliable information regardless of physical obstacles or environmental conditions. This method ensures complete comprehension of regulatory information, which is essential for establishing clear legal responsibility frameworks in autonomous driving environments.

In this study, we propose a data-driven analytical framework for systematically analyzing the driving patterns of autonomous buses and quantitatively identifying risky driving behaviors at the road-segment level using operational data from real roads. The analysis was based on Basic Safety Message (BSM) data collected over 125 days from two Panta-G autonomous buses operating in the Pangyo Autonomous Driving Testbed. Key driving indicators included speed, acceleration, yaw rate, and elevation, which were mapped onto high-definition (HD) road maps. A hybrid clustering method combining self-organizing map (SOM) and k-means++ was applied, which resulted in eight distinct driving pattern clusters. Among these, four clusters exhibited characteristics associated with risky driving such as sudden acceleration, deceleration, and abrupt steering, and were spatially visualized using digital maps. These visualizations offer practical insights for real-time monitoring and localized risk assessment in autonomous vehicle operations. The proposed framework provides empirical evidence for evaluating the operational safety and reliability of autonomous buses based on repeated behavioral patterns. Its adaptability to diverse urban environments highlights its utility for intelligent traffic control systems and future mobility policy planning.

In this study, we analyzed and projected the future toll revenue of privatized expressways in Korea as their concession periods expire to propose legal and institutional improvements for effective public reinvestment. We focused on 17 expressways that are privately operated and currently in service as of late 2023. We constructed multiple scenarios extending from 2031 to 2050 to anticipate how toll revenues might evolve over time. Although these projections could prove inaccurate or anachronistic, we considered a range of outcomes. Legal frameworks such as the Toll Road Act and the Private Investment Act were also examined to explore whether reinvestment of toll income would be legally viable after the end of the concession agreements. We considered a scenario in which toll rates remained unchanged after the concession expired. In such a case, projected net revenues might reach roughly KRW 1.319 trillion by 2031 and increase to about KRW 10.146 trillion by 2050. That value is roughly KRW 11.8 trillion over two decades—a considerable sum. To put this in context, as of the end of 2023, this figure would represent a substantial slice of the total market capitalization of private expressways. On the other hand, lowering the tolls to match those of public roads would cut the revenue nearly in half. Of note, current laws mostly restrict toll income to basic maintenance; any broader use would require legislative reform. Legal revisions are needed to allow the reinvestment of surplus toll revenue from expired concessions into new SOC projects. Additionally, measures such as toll adjustment and integrated toll system management are recommended to enhance public benefit and investment sustainability.

This study evaluates how road profile and speed affect tire loads of a hydrogen tube trailer using MSC Adams/Car multibody dynamics simulation. A tractor and trailer loaded with 64 high-pressure cylinders were modeled, and four representative road profiles flat, pothole, short-wave, and long-wave were applied at 30, 60, and 80 km/h. Vertical tire load time histories were extracted for five wheel positions. Flat roads yielded stable loads matching static distribution. Potholes produced short, high-amplitude impacts (up to 120 kN at 30 km/h) with reduced peaks at higher speeds. Short-wave profiles caused severe asymmetric roll loads (67 kN at 80 km/h), while long-wave inputs generated smoother, moderate increases over longer durations. Load amplification diminished toward trailer axles due to suspension energy dissipation. The results inform structural design of tube trailers and development of speed-control or active load-mitigation strategies for autonomous hydrogen transport vehicles.

This study aims to analyze the mitigation effects of phantom traffic jams on highways in a mixed traffic environment in which autonomous vehicles (AVs) and human-driven vehicles coexist. It focuses on identifying the key factors that contribute to phantom congestion and evaluating the extent to which the introduction of AVs can stabilize traffic flow and alleviate nonrecurring congestion. To achieve this goal, a theoretical analysis was conducted to examine the major causes of phantom traffic jams, including variations in the vehicle speed, road gradients, driver behaviors (for example, acceleration and deceleration), and visual adaptations in tunnel sections. Based on these factors, simulation scenarios were constructed using VISSIM to replicate real-world conditions in highway tunnel segments. The scenarios varied according to the AV penetration rate (0%, 20%, 40%, and 60%) and incorporated key traffic indicators such as the vehicle composition, speed, and headway. Traffic flow stability was evaluated using metrics including the average travel speed, headway consistency, and frequency of acceleration and deceleration events across sections. The simulation results showed that as the proportion of AVs increased, the average travel speed improved, and both the headway stability and flow continuity were enhanced. In particular, tunnel segments with higher AV ratios experienced fewer deceleration events and reduced behavioral variability, contributing to a more stable traffic flow. These findings suggested that AVs could play a critical role in mitigating phantom traffic jams by maintaining steady speeds and safe following distances, thereby reducing the instability caused by human driving behaviors. This study offers a foundational reference for future traffic congestion mitigation strategies and AV policy development, particularly in anticipation of increasingly mixed traffic environments.

This study investigated the legibility distance characteristics of variable speed limit signs and variable message signs under foggy conditions according to different luminance levels. In South Korea, the current installation standards for these signs are based on normal weather conditions, and empirical analyses of their visibility under adverse weather conditions remain limited. To address this issue, a controlled large-scale experiment was conducted at the Yeoncheon SOC Test Center, where artificial fog was generated in a tunnel environment. Seventeen elderly participants (average age: 70 years) participated in the experiment. They walked toward a sign to identify the distance at which it became legible. The experiment varied the fog visibility levels (50–80 m, 30–50 m, and 10–30 m) and display luminance (six levels). The results showed that as the fog density increased (that is, visibility decreased), the average legibility distance decreased. Conversely, higher luminance levels consistently improved legibility distance under foggy conditions. Under normal weather conditions, changes in luminance had a minimal impact on legibility. Compared with the minimum legibility distances calculated based on the design speed, many cases under foggy conditions failed to meet these thresholds, particularly at lower luminance levels. These findings indicate that the current luminance standards may not adequately ensure sign legibility under adverse weather conditions, underscoring the need for updated luminance guidelines that reflect environmental conditions. The results of this study provided quantitative data to support policy revisions and technical advancements aimed at improving road safety.

본 연구는 우리나라 산림 보호지역 내 도로 밀도를 분석하여, 산림 보전과 관리의 효율성을 높이기 위한 시사점을 도출하고자 하였다. 도로는 산림 관리에 필수적인 기반 시설이지만, 보호지역 내에서는 생태계를 파편화하고 서식지를 훼손할 수 있다. 특히 보호지역의 보전 목적과 실제 관리 현황 사이의 괴리를 파악하기 위해서는, 국제적으로 통용되는 기준에 따른 비교 분석이 필요하다. 이에 본 연구는 IUCN 보호지역 카테고리를 기준으로 유형별 도로 밀도 차이를 분석하고, 각 유형의 보전 목적에 부합하는 관리 실태를 평가하였다. 연구 결과, 우리나라 산림 보호지역의 평균 도로 밀도는 19.6m/ha로 나타났으며, 이는 국내 선행연구에서 제시된 지속 가능한 산림경영을 위한 적정 임도 밀도 범위 (5.12m/ha~14.01m/ha)를 크게 초과하는 수치였다. 보전 목적이 가장 엄격한 카테고리 Ⅰa에서도 도로 밀도는 52.6m/ha 에 달해, 보전 취지에 부합하지 않는 과도한 개발 수준을 보였다. 자원의 지속 가능한 이용을 목적으로 하는 카테고리 Ⅵ 또한 29.1m/ha의 비교적 높은 도로 밀도를 기록하였다. 이는 보호지역의 지정 유형과 관계없이 도로망이 과도하게 발달되어 있음을 시사한다. 이와 같은 높은 도로 밀도는 보호지역 내 생물다양성 보전에 부정적인 영향을 미치며, 서식지 단절과 생태계 교란을 가중시킬 수 있다. 따라서 보호지역 내 불필요한 도로를 단계적으로 폐쇄하고 생태적으로 복원하는 정책이 시급하며, 보호지역의 법적 기준 또한 생태적 요구에 부합하도록 개선할 필요가 있다.

Autonomous vehicle (AV) technology is rapidly entering the commercialization phase driven by advancements in artificial intelligence, sensor fusion, and communication-based vehicle control systems. Real-world road testing and pilot deployments are increasingly being conducted, both domestically and internationally. However, ensuring the safe operation of AVs on public roads requires not only technological advancement of the vehicle itself but also a thorough pre-evaluation of the road environments in which AVs are expected to operate. However, most previous studies have focused primarily on improving internal algorithms or sensor performance, with relatively limited efforts to quantitatively assess how the structural and physical characteristics of road environments affect AV driving safety. To address this gap, this study quantitatively evaluated the compatibility of road environments for AV operation and defined the road conditions under which AVs can drive safely. Three evaluation scenarios were designed by combining static factors such as curve radius and longitudinal gradient with dynamic factors such as level of service (LOS). Using the MORAI SIM autonomous driving simulator, we modeled the driving behaviors of autonomous vehicles and buses in a virtual environment. For each scenario, the minimum time to collision (mTTC) from the moment the AV sensors detected a lead vehicle was calculated to assess risk levels across different road conditions.The analysis revealed that sharper curves and lower service levels resulted in significantly increased risk. Autonomous buses exhibited a higher risk on downhill segments, autonomous vehicles were more vulnerable to uphill slopes and gradient transitions. The findings of this study can be applied to establish road design standards, develop pre-assessment systems for AV road compatibility, and improve AV route planning and navigation systems, thereby providing valuable implications for policy and infrastructure development.

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.