This study assessed the feasibility of deploying mobile safety-sign robots to replace human flaggers in highway work zones and determined the optimal Dynamic Message Display (DMD) configurations. The study consisted of two phases. The first phase involved a pilot test on a test road in Yeoju, where the work zone conditions were replicated by following the highway work zone traffic management guidelines. Eight drivers participated in a pilot test. All four driving behavior indicators demonstrated improvements in driving safety under the robotbased scenario compared with the conventional human flagger scenario. The second phase adopted a Virtual Reality (VR)-integrated Driving Simulator (DS) to analyze the driver behavior across various DMD types. Six robot-based scenarios were designed by combining three DMD message types with two display sizes along with one baseline scenario based on existing guidelines for comparison. Twenty drivers participated in this experiment. A rank-based comparative analysis incorporating five evaluation indicators was performed to derive the optimal DMD display type. Scenario 3 (vertical ‘60’ display) and Scenario 6 (horizontal ‘감속60’ display, ‘Reduce speed to 60’ in English) were identified as the optimal DMD display types. These findings establish a foundation for the development of traffic management standards for safety sign robots in highway work zones.

This study proposes a statistical modeling framework for estimating the daily number of bus stops at highway transfer facilities (ex-HUBs) where demand information is often uncertain during the early planning stages. Accurate estimation of the daily number of bus stops is critical for efficient design and operation; however, reliable demand data are rarely available in the initial planning phase. Using pooled data from 16 facilities, a direct demand estimation approach was implemented, based on facility characteristics, transportation connectivity, highway traffic conditions, and socioeconomic factors. Log-linear model (LLM) and negative binomial model (NBM) were developed to capture the count data characteristics. Ensemble models using arithmetic and weighted means were also constructed to improve predictive reliability. The analysis revealed that the arithmetic mean ensemble of NBM and LLM produced the most accurate predictions. The daily number of bus stops was significantly influenced by the distance from bus terminals, highway traffic volume, public transportation connectivity, economically active population, and level of urbanization. The framework proposed in this study provides a practical tool for estimating the daily number of bus stops at highway transfer facilities, and can support more reliable feasibility analyses and infrastructure planning under demand uncertainty.

Potholes on urban expressways are a critical pavement maintenance problem because they threaten driving safety, generate vehicle-damage claims, and require repeated emergency repairs. However, network-level evidence integrating climate, traffic, maintenance execution, and detection practice remains limited. This study addressed this gap through a stage-1 empirical assessment of pothole occurrence and pavement maintenance response on the Seoul urban expressway network. The novelty lies in integrating six years of operational data, including pothole repair records, compensation cases, monthly rainfall, monthly average temperature, route-level traffic volume, maintenance budget and execution records, detection pathways, and repeated pothole locations. A total of 28,821 pothole repairs were recorded between 2020 and 2025, with Olympic-daero (11,330 cases), Dongbu Trunk Road (6,594 cases), and Gangbyeonbuk-ro (5,067 cases) accounting for approximately 79.8% of the total. The compensation burden was also concentrated, with 158 cases and a total payout of KRW 48,592,000. Pothole occurrence showed a clear dual-season pattern, with high counts during the thawing period and a stronger summer peak, increasing from 1950, 3100, and 3773 cases in June, July, and August when rainfall rose from 174.60 mm to 333.68 mm and 352.15 mm, respectively. Traffic remained consistently high (48,576–96,700 vehicles/day) but varied by only approximately 5.1% annually, indicating that climate governed outbreak timing, while traffic acted mainly as a chronic aggravating factor. Artificial intelligence (AI)-based Camera Detection System (CDS) detection contributed to 54.3% and 57.2% of external detections in 2024 and 2025, respectively, while repeated repairs accounted for 3,957 cases across 783 locations (13.7% of total repairs). These findings support seasonal preventive maintenance, route-based prioritization, AI-assisted detection, and hotspot-focused management.

국내 고속도로 콘크리트 포장은 주로 줄눈 콘크리트 포장(JCP) 형식으로 시공되어 줄눈부 파손에 따른 유지관리 부담이 지속되고 있으며 이를 보완하기 위해 연속철근 콘크리트 포장(CRCP)이 확대 적용되고 있다. 하지만 기존의 노후화된 JCP를 CRCP로 전환하는 기술에는 한계가 있는 실정이다. 이에 본 연구에서는 공용성과 시공성을 동시에 확보하기 위해 CRCP 형 식과 프리캐스트 콘크리트 포장 공법을 접목한 프리캐스트 CRCP 슬래브를 설계하였다. 슬래브 내부의 철근비를 0.68%로 설계하여 배근하였으며 타이바 포켓은 슬래브 측면 중앙부에 배치하도록 설계하였다. 유한요소해석과 모멘트 분포 분석을 수행하여 슬래브 상부에 최적 인양 위치를 선정하였으며 매립형 인양 장치를 배치하였다. 또한 그라우트 주입구는 차선 기 준으로 슬래브 외곽부 중앙에 위치하도록 설계하였다. 슬래브의 연결부는 덮개 형식으로 구성하였으며 상부 덮개에는 앵커 를 설치하여 그라우트의 탈락을 방지하였다. 연결부에는 연속철근을 노출시켜 인접되는 슬래브와의 거동이 일체화되어 CRCP의 특성을 발휘하도록 설계하였다.

최근 도로 포장 분야에서는 시공성과 공용성 확보를 위해 모듈러 형식의 프리캐스트 콘크리트 포장 공법을 적용하는 추 세이다. 프리캐스트 콘크리트 포장은 사전 제작한 슬래브를 현장에서 조립 및 시공함으로써 시공 시간을 단축할 수 있어 장 기간 교통통제가 어려운 구간의 신속한 유지보수에 활용되고 있다. 국내에서는 도심지 버스정류장을 중심으로 적용 사례가 증가하고 있으나 고속도로에 적용된 사례는 미비한 실정이다. 이에 본 연구에서는 국내 고속도로 환경에 적합한 프리캐스트 콘크리트 포장 시공 방안을 마련하기 위해 시험시공을 수행하고자 줄눈 콘크리트 포장(JCP) 형식의 프리캐스트 슬래브를 설 계 및 제작하였다.

국내 고속도로에 적용된 콘크리트 포장은 오랜 공용기간으로 인해 노후화되어 유지보수가 필요한 구간이 증가하고 있다. 이러한 구간의 유지보수를 위해 국외에서는 프리캐스트 콘크리트 포장 공법을 사용하여 노후화된 구간을 보수하고 있으며 국내 고속도로에도 프리캐스트 포장의 적용이 필요한 실정이다. 본 연구에서는 고속도로 환경에 적합한 프리캐스트 콘크리 트 포장의 시공 방안 개발을 목적으로 현장 조사를 수행하여 시험시공 계획을 수립하였다. 시험시공 구간은 서해안 고속도 로 비봉 영업소 구간의 화물차 전용 차로로 선정하여 수차례의 현장 조사를 수행하였다. 현장 조사 결과, 시험시공에 적용 될 슬래브의 제원은 연장 3.0m, 폭 5.1m, 두께 0.29m로 선정하였으며 CRCP 형식과 JCP 형식으로 프리캐스트 포장을 구성 하는 시험시공 계획을 수립하였다.

도로의 기하구조(종단경사, 평면 곡선반경)는 차량의 속도 변화, 제동 거리, 원심력 등에 직접적인 영향을 미쳐 주행 안전성과 사고 위험을 결정짓는 핵심적인 설계 요소이다(Park et al., 2008). 따라서 도로 유지관리 측면 에서 이러한 기하선형 정보를 정밀하게 측정하고 관리하는 것은 필수적이나, 준공 후 장기간이 경과하거나 관 리 체계가 다원화된 경우 데이터가 누락되어 통합적인 활용에 한계가 있다. 이에 본 연구는 설계도면이 부재한 대규모 도로망의 안전 진단 및 위험 구간 판단 근거를 마련하기 위해, GIS(Geographic Information System) 기반 노드·링크 시스템의 평면선형 데이터와 공개 DEM(Digital Elevation Model)을 활용하여 전국 고속국도의 기하구조를 추정하는 경제적이고 보편적인 방법론을 제안하고 자 한다.

본 연구는 3차원 비선형 유한요소해석을 이용하여 고속도로 2주형 교각 코핑부에서 철근을 유리섬유보강폴리머(GFRP) 보강 근으로 대체하는 경우를 평가하였다. 콘크리트의 균열, 손상 및 보강근 응답을 모사하기 위해 콘크리트 손상소성(CDP) 모델을 적용하 였다. 단조하중 조건에서 철근 기준 Case와 다수의 GFRP Case를 비교하였다. 주요 변수로는 GFRP의 강성, 콘크리트와의 부착계수 영향, 그리고 수직 전단보강근 상세 배근을 포함하였다. 수치해석 모델은 실험 경향과의 비교를 통해 검증되었으며 전반적인 거동이 일관되게 나타났다. GFRP로의 대체는 철근 대비 강성과 하중 전달 메커니즘을 변화시켰다. 또한 콘크리트 손상이 전체 응답과 파괴 진행을 지배하는 주요 요인으로 나타났다. GFRP 강성이 높고 부착성능이 우수할수록 구조 효율과 상세설계의 실현성이 향상되었다. 적절한 설계가 전제될 경우 전단보강근의 양은 전체 거동에 미치는 영향이 제한적인 범위에서 최적화가 가능하였다. 이상의 결과는 GFRP 적용의 실무적 가능성을 뒷받침하는 동시에, GFRP의 선형탄성ㆍ취성 거동과 국부 응력집중 가능성을 고려할 필요가 있음을 시사한다.

Using highway accident data, this study predicts the probability of rollover, overturning, and fire accidents and identifies the related risk factors. Whereas existing studies rely primarily on limited explanatory variables and classical statistical models, this study simultaneously enhances predictive performance and interpretability by applying and comparing machine learning-based nonlinear prediction-analysis systems (XGBoost and Shapley additive explanations) with logistic regression, which offers advantages in statistical reasoning. The analysis identifies speeding, segment characteristics (tunnel, ramp, shoulder), and vehicle type (SUV, truck, trailer, and tank lorry) as common key risk factors. These results suggest the necessity of establishing a multilayered management system for speeding, improving facilities centered on high-risk sections (tunnel in/out, ramp, and downhill), performing custom inspections for each vehicle type (load, tire, and brake system), and improving driving behavior (enhancing forward attention, introducing a drowsiness warning system, etc.). This study provides a datadriven empirical basis for identifying the causes of major highway accidents and for designing effective prevention policies.

Truck platooning technology, which utilizes vehicle-to-vehicle communication to enable two or more autonomous trucks to travel in a platoon, is garnering attention. However, before platooning is implemented, an environment that can stably maintain a constant speed must be established. Therefore, maintaining a constant speed is a key prerequisite for truck platooning. To overcome the limitations of previous studies, which relied on traffic simulations or limited experiments, this study analyzes second-by-second truck DTG driving records obtained from highways near major domestic ports. Based on these data, a sliding-window technique was employed to detect constant-speed driving patterns and estimate the rate of constant-speed driving by section. The analysis revealed a high rate of constant-speed driving at the Noeun JCT–Dongcheongju IC, where the traffic volume was low and the road alignment was gentle. However, a low rate was observed at the Gunpo IC–Donggunpo IC, where ramp entries and exits were frequent. Subsequently, a multivariate fractional polynomial model was employed to analyze factors influencing constant-speed driving. The main factors identified were speed dispersion, average duration of constantspeed driving, and volume of large trucks per lane. This shows that speed stability, continuity of driving patterns, and vehicle composition within a section are more important factors in determining constant-speed driving than the average driving speed or traffic volume. This study is significant because it empirically elucidates the characteristics and factors influencing constant-speed driving using large-scale field data. Furthermore, it is expected to provide fundamental data for selecting suitable sections for truck platooning and establishing logistics efficiency policies.

In response to the contemporary demands of the construction industry for climate-change action and carbon neutrality, this study conducts a comprehensive analysis of the applicability of Portland limestone cement (PLC)—a notable sustainable alternative to ordinary Portland cement (OPC)—for highway pavement applications. PLC is an eco-friendly material that reduces carbon-dioxide emissions and energy consumption compared with OPC by reducing the clinker ratio in its manufacturing process. This study examines the fundamental physical and chemical mechanisms of PLC concrete and compares its mechanical performance and durability characteristics with those of OPC concrete. The results indicate that PLC concrete exhibits performance levels equivalent to or superior to those of OPC in key metrics such as compressive and flexural strengths, with particularly outstanding performance in durability aspects such as chloride-penetration resistance. However, the potential for early-age cracking and compatibility issues with certain admixtures are identified as challenges that must be addressed for the wider field application of PLC concrete. Thus, this study proposes the integration of nanotechnology to overcome these technical limitations and maximize performance. Specifically, methods to significantly improve the strength, abrasion resistance, fatigue resistance, and crack-control performance by utilizing nanomaterials such as Nano- , Nano- , and graphene oxide ( ) to control the microstructure of PLC concrete are presented. Finally, a comprehensive roadmap is proposed to enhance the field applicability of PLC concrete for highway pavements and contribute to the construction of sustainable social infrastructure through three key strategies: mix design optimization, consideration of regional environmental conditions, and integration of nanotechnology.

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 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 aimed to evaluate the effect of key operational factors on traffic performance in long underground expressways. This study was motivated by the increasing policy interest in underground expressway infrastructure as a solution to chronic surface-level congestion in dense urban regions. A scenario-based microscopic traffic simulation was conducted using VISSIM considering combinations of traffic volume, proportion of heavy vehicles, and longitudinal slopes. A total of 72 scenarios were simulated, and the weighted average speed and total throughput were analyzed. The simulation results showed that the entry traffic volume and longitudinal gradient significantly affected the average speed, particularly in uphill exit segments. The heavy vehicle ratio also contributed to consistent reductions in speed. However, the overall throughput remained relatively stable despite variations in heavy vehicle proportions, suggesting that speed is more sensitive to flow composition than to volume capacity. Although interaction effects were not statistically tested, the combined scenario trends suggested that steeper slopes and high heavy-vehicle ratios jointly intensify speed reduction. These findings support the early-stage design and traffic planning of underground expressways.