Wet pavement friction decreases with an increase in water film thickness (WFT), leading to a significant increase in vehicle crashes. The British pendulum test described in ASTM E303-93 is a method used to measure the pavement friction under wet conditions for the input of geometric design and pavement management systems. The British pendulum number (BPN) under wet conditions varies with WFT. Following the ASTM E303-93 standard procedure, WFT was simulated by spraying water onto the pavement surface. However, the measurement of the BPN did not include specific information regarding the WFT present during testing. To address these issues, WFTs and BPNs are measured using artificial rainfall generated by a rainfall simulator across various intensities, drainage lengths, pavement slopes, and pavement surfaces. This study aims to investigate the influence of the WFT on the BPN for wet pavement friction and provide the WFT corresponding to each BPN measurement for different surface types. The BPNs and WFTs of three test slabs, including diamond grooving and tining surfaces with 16 mm and 25 mm spacing, were measured under wet conditions by spraying water and creating WFTs using a rainfall simulator. Measurements were taken in both longitudinal and transverse directions, considering different rainfall intensities (40 mm/h, 80 mm/h, and 130 mm/h), pavement slopes (2%, 5%, and 10%), and drainage path lengths (1 m, 2 m, 3 m, 4 m, and 5 m). The test results indicated that wet pavement friction decreased as the WFT increased that was influenced by several factors including the pavement slope, mean texture depth, rainfall intensity, and drainage path length. Specifically, the WFT tended to increase with a decrease in the pavement slope and an increase in the mean texture depth, rainfall intensity, and drainage path length. In particular, surface texture played a significant role in the wet friction performance, with diamond-grooved pavements. Among the tested surfaces, the diamond-grooved (longitudinal and transverse) pavements demonstrated a more effective wet friction performance, maintaining higher BPN values across varying WFT levels. Conversely, longitudinally and transversely tined surfaces with 25 mm spacing showed a more significant decrease in BPN, reflecting a higher sensitivity to WFT. In contrast, tined surfaces with 16-mm spacing exhibited a more gradual reduction in friction, likely owing to enhanced drainage and better resistance to water-induced friction loss. Additionally, these results indicated that longitudinal textures demonstrated a more significant reduction in friction with increasing WFT compared with transverse textures. This demonstrated that the texture type, direction, and spacing significantly influenced the friction loss under wet conditions, with diamond grooving offering the best overall performance. This study highlighted the critical role of WFT in pavement friction design, emphasizing the need to consider the WFT for a more accurate assessment of wet pavement friction. The WFT was influenced by factors such as the pavement slope, rainfall intensity, drainage path length, and surface texture. The diamond-grooved pavements demonstrated a more effective wet friction performance, maintaining higher BPN values across varying WFT levels. In contrast, tined surfaces with larger spacings exhibited more significant friction loss, whereas those with smaller spacings showed a more gradual reduction, likely owing to better drainage. In particular, longitudinal textures showed a greater reduction in friction compared with transverse textures. Overall, the texture type, direction, and spacing played crucial roles in wet friction performance, with diamond grooving offering the best results.
This study investigated the vertical displacement behavior caused by differential drying shrinkage in jointed concrete pavements. This study proposed a method to convert this behavior into an equivalent linear temperature difference for structural analysis. Controlled experiments were conducted under varying humidity and airflow conditions to simulate pavement environments. The test results showed that a lower relative humidity and added airflow significantly increased the vertical displacement, particularly at the slab edges. A 3D finite element model using ABAQUS was developed to analyze the behavior and derive the equivalent linear temperature difference that increased with curing age and varied notably with environmental conditions. These findings highlighted the impact of early-age environmental factors on the shrinkage behavior and suggested that the proposed method offered a practical approach for predicting deformation without repeated physical testing.
This study proposes a methodology for predicting properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, this study investigates the variations in molecular dynamics (MD) results with molecular structural complexity and assesses the independence and correlation of variables that influence density. In this study, MD simulations were performed on hydrocarbon-based and individual asphalt binder molecules. The effects of various temperatures, molecular conditions, and structural features on the density were analyzed. MD-related variables influencing the calculated density were evaluated and compared with experimentally measured densities. The MD-calculated densities were used as target variables in a subsequent study, in which a machine learning model was applied to perform quantitative structure–property relationship analysis.The MD-calculated densities showed a strong correlation with experimental measurements, achieving a coefficient of determination of R2 > 0.95. Potential energy exhibited a tendency to cluster into 4–6 groups depending on the molecular structure. In addition, increasing molecular weight and decreasing temperature led to higher density and viscosity. Torsional energy and other individual energy components were identified as significant factors influencing both potential energy and density. This study provided foundational data for the property prediction of asphalt binders by quantitatively analyzing the relationship between the molecular structure and properties using MD simulations. Key features that could be used in the construction of polymer structure databases and AI-based material design were also proposed. In particular, the integration of MD-based simulation and machine learning was confirmed to be a practical alternative for predicting the properties of complex polymer composite systems.
This study proposes a methodology for predicting the physical properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, features are constructed using molecular dynamics (MD) simulation results and descriptor calculation tools. This study investigates the changes in the calculated density depending on the characteristics of the training dataset and analyzes the feature characteristics across datasets to identify key features. In this study, 2,415 hydrocarbon and binder-derived polymer molecules were analyzed using MD simulations and 2,790 chemical descriptors generated using alvaDesc. The features were pre-processed using correlation filtering, PCA, and recursive feature elimination. The XGBoost models were trained using k-fold cross-validation and Optuna optimization. SHAP analysis was used to interpret feature contributions. The variables influencing the density prediction differed between the hydrocarbon and binder groups. However, the hydrogen atom count (H), van der Waals energy, and descriptors such as SpMAD_EA_LboR consistently had a strong impact. The trained models achieved high accuracy (R² > 0.99) across different datasets, and the SHAP results revealed that the edge adjacency, topological, and 3D geometrical descriptors were critical. In terms of predictive accuracy and interpretability, the integrated MDQSPR framework demonstrated high reliability for estimating the properties of individual binder polymers. This approach contributed to a molecular-level understanding and facilitated the development of ecofriendly and efficient modifiers for asphalt binders.
This study aims to quantitatively evaluate the life cycle carbon emissions of continuously reinforced concrete pavements on Korean expressways. The analysis focuses on assessing the effect of the changes in pavement design life and maintenance frequency on total carbon emissions to provide a basis for effective carbon reduction strategies. In accordance with ISO 14040 and ISO 14044, carbon emissions were calculated using actual design documents, including bills of quantities and unit price lists. National emission factors were applied to each life cycle stage, including the maintenance stage that was modeled based on the standard maintenance scenarios of the Korea Expressway Corporation. The study also conducted a scenario-based evaluation to examine the impact of extending the pavement design life from 20 to 30 years on maintenance-related emissions. The usage stage accounted for the largest share of total emissions, followed by the material production and maintenance stages. Notably, repeated asphalt overlay maintenance contributed significantly to emissions. Extending the design life reduced the number of high-emission maintenance activities, leading to a significant reduction in the total life cycle emissions. Extending the pavement design life and optimizing maintenance cycles were effective strategies for reducing the life cycle carbon emissions in road infrastructure. Furthermore, applying eco-design principles—such as incorporating recycled aggregates or low-carbon cement during the design stage—could further enhance sustainability. Future research should include various case studies and support the development of standardized national life cycle inventory databases for road infrastructure systems.
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.
Ensuring sufficient road surface friction is essential for maintaining traffic safety, particularly under adverse weather conditions. However, the current Korean design standards for the longitudinal friction coefficient (LFC) are primarily derived from foreign guidelines with limited empirical validation under domestic road and climatic conditions. This study aims to generate field-based LFC data across a range of road surface types and vehicle speeds and evaluate the appropriateness of the LFC values currently adopted in national road design standards. The field experiments were conducted at the Yeoncheon SOC Proving Ground. The five representative road surface conditions were dry, wet, slush, compacted snow, and icy. Driving tests were conducted for each surface condition at four speeds (30, 40, 50, and 60 km/h) using a test vehicle equipped with continuous friction measuring equipment. Twenty scenarios were tested with multiple repetitions to ensure statistical reliability. Consequently, the average LFC values were 0.748 for dry, 0.615 for wet, 0.232 for slush, 0.173 for compacted snow, and 0.133 for icy conditions. The results showed that road surface conditions had a significantly greater impact on the LFC than vehicle speed. Moreover, the values measured under low-friction conditions were consistently lower than those specified in the current Korean standards. These findings highlight the need to revise the existing LFC design values to better reflect domestic winter road environments. The data can also inform the development of road safety strategies such as variable speed limit systems, autonomous vehicle control algorithms, and automatic emergency braking systems suitable for winter operations.
As a key axis of metropolitan public transport, exclusive median bus lanes (EMBLs) are facing operational limits owing to urban expansion and increased traffic demand, with queues at bus stops during peak hours causing severe delays. This study aims to empirically identify the phenomenon of queue-based delays at the stop level, that is difficult to explain using conventional capacity calculation methods, and to propose an operational strategy for its mitigation. By realistically assessing passenger inconvenience through a revised “additional passenger travel time” calculation based on bus travel, this study provides a balanced analysis of the tradeoff between system efficiency and passenger convenience, thereby contributing to the development of sustainable urban transit systems. This study compared current all-stop operations with two skip-stop scenarios in Songpa-daero, a major arterial corridor in Seoul. Using an actual bus management system and transit card data, key performance indicators including queue length, travel time, dwell time, and additional passenger travel time were analyzed. Scenario I applied an A/B service-style alternating stop operation, whereas Scenario II implemented a hybrid approach, designating hub stops at key locations. Simulation modeling was used to evaluate the system-wide impacts during peak hours. The analysis revealed that skip-stop operations had significant potential to improve EMBL performance; however, the benefits were subject to a trade-off with passenger inconvenience. Scenario I with alternating stops was most effective in reducing the queue length and overall travel time. However, it also resulted in the largest increase in additional passenger travel time calculated with the revised methodology. In contrast, Scenario II with hub stops, while showing slightly less improvement in operational efficiency, presented a more balanced outcome by mitigating the burden of additional travel time for passengers through hub stops, thereby enhancing service equity. Both scenarios showed reduced dwell times at most stops, indicating the alleviation of boarding and alighting congestion. This study confirmed that skip-stop strategies could effectively improve the operational efficiency of EMBLs by reducing queue lengths and travel times. However, the additional passenger travel time, including bus transfers, is a critical factor that must be considered. Scenario I was evaluated as superior for maximizing the operational efficiency, whereas Scenario II was a better alternative for securing a balance with passenger convenience. This study is significant because it presents an analytical framework for quantifying queue-based delays and realistically assessing passenger impact. Although limitations remain, such as not fully capturing the complex decision-making processes of actual passengers, the methodology and findings offer practical guidance for urban transport planners seeking data-driven solutions to EMBL congestion, emphasizing the importance of the passenger perspective in skip-stop strategy design.
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.
Current portable reference equipment used to evaluate the performance of vehicle detectors can collect traffic volume and speed only for the outermost lanes in each direction. Passing vehicles on the other lanes are manually counted by reviewing the recorded videos. Consequently, only traffic volume—without vehicle speed—is used as a reference value. This method is time-consuming for comparing the performance data from the equipment with the reference data and can compromise the performance evaluation. This study aims to enhance the efficiency of vehicle detection system (VDS) performance evaluations by developing multilane portable reference equipment that can accurately collect traffic information for lanes beyond the outermost lane or for more than two lanes. This study introduced the core technologies of multilane portable reference equipment and compared and analyzed the measurement accuracy of the developed equipment against data from fixed reference equipment operated by the Intelligent Transportation System (ITS) Certification and Performance Evaluation Center, following ITS performance evaluation criteria. The data from the fixed reference equipment were considered the true values, providing a basis for evaluating the accuracy of the measurements by the developed equipment. First, the accuracy of the vehicle length was determined by driving four test vehicles, each measuring 7,085 mm in length, 24–29 times in each lane. The accuracy was calculated by comparing the vehicle length data obtained from the fixed reference equipment with the actual vehicle length. A confidence interval was established for this accuracy. To assess the accuracy of the speed and occupancy time in relation to the accuracy of the analyzed vehicle length, we evaluated the error range of the vehicle length according to variations in speed and occupancy time. This analysis was based on the following relationship equation: “vehicle length = speed × occupied time – sensor spacing.” The analysis used data from approximately 16,000 vehicles, including the speed, occupancy time, and vehicle length, collected between 8:00 am and 12:00 pm on August 8, 2024. The principle behind measuring traffic volume and vehicle speed using multilane portable reference equipment involves detecting a vehicle by analyzing the time difference between the driver and passenger tires. The vehicle speed was calculated using the installation angle of the tire detection sensor and trigonometric functions. An analysis of the measurement accuracy revealed that the traffic volume accuracy of the outermost lane (the fourth lane) was 100% during both day and night. The speed accuracy was 98.8% during the day and 97.7% at night, representing the highest performance in these metrics. Additionally, the traffic volume accuracy for the innermost lane (the first lane), as measured by the detection sensor from the third lane, was more than 99.3% at all times, with a speed accuracy exceeding 96% during the day and night, that also demonstrated excellent results. The analysis results indicated that the multilane portable reference equipment developed in this study was suitable for evaluating the VDS performance. This equipment allowed the collection of traffic volume and speed data from all lanes, rather than only the outermost lanes. This capability enabled consistent analysis for each lane and enhanced efficiency by reducing the analysis time. Additionally, this is expected to improve the reliability of the performance evaluations.
This study proposed and empirically validated an integrated conceptual model combining protection motivation theory (PMT) and the theory of planned behavior (TPB) to explain the policy acceptance of special evacuation stair installation and the evacuation intentions of users in deep subway stations. An online survey was conducted among metropolitan subway riders (18 items total, 15 core items), and data were analyzed using SPSS Statistics 26.0 for exploratory factor analysis (EFA), multiple regression, K-means cluster analysis, and chi-square tests. EFA confirmed a four-factor structure—awareness, perceived feasibility and trust, behavioral intention, and policy acceptance—with Cronbach α ≥ 0.78 for all factors. Regression results indicated that attitude and perceived behavioral control significantly predicted behavioral intention (p < 0.001) that in turn demonstrated strong explanatory power for policy acceptance (p < 0.01). Cluster analysis identified three user typologies (“high awareness–high acceptance,” “moderate awareness–moderate acceptance,” and “low–awareness–low acceptance”), and chi-square tests revealed significant group differences in prior training and in-depth guidance participation (p < 0.05). The findings suggested that the integrated PMT–TPB model effectively captured the determinants of evacuation stair acceptance and intention, providing a foundation for tailored communication and training strategies.
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.
Parking lots are environments in which various types of vehicles such as passenger cars, trucks, and personal mobility (PM) devices coexist. Despite numerous studies analyzing parking lot crash characteristics, PM devices have received limited attention. Analyzing parking lot crash characteristics involving PMs is essential, given that the market penetration rate of PM is increasing. This study quantitatively analyzed the severity characteristics of vehicle-to-PM crashes in parking lots by aggregating the number of crashes and identified factors influencing crash severity through the development of an ordered probit model. According to the quantitative analysis results, PM users experienced a higher crash severity owing to insufficient personal protective equipment. Additionally, crashes involving parked or illegally parked PMs tended to have a lower severity than those involving PMs being ridden. The developed crash severity model identified several key factors, including crashes related to illegally parked vehicles, crashes in which a vehicle was at fault for a collision with a parked PM, cases in which the vehicle type of the at-fault driver was a passenger car, and cases in which the at-fault driver was a female. A higher probability of property damage crashes, rather than injury or fatal crashes, was observed in cases involving parked or illegally parked PMs owing to lower relative speeds. Passenger cars generally have shorter braking distances than trucks or buses, allowing quicker responses to sudden situations. A female at-fault driver may experience longer perception–reaction times, potentially increasing the probability of injury or fatal crashes. The findings of this study can provide foundational data for revising ordinances or laws to enhance parking lot safety or preliminary data for developing parking lot management systems. Furthermore, the identified crash severity factors can be prioritized to develop effective measures for crash prevention and severity reduction in parking lots.