간행물
한국도로학회논문집 KCI 등재 International journal of highway engineering
- 발행기관 한국도로학회
- 자료유형 학술지
- 간기 격월간
- ISSN 1738-7159 (Print)2287-3678 (Online)
- 수록기간 1999 ~ 2025
- 주제분류 공학 > 토목공학 공학 분류의 다른 간행물
- 십진분류KDC 534DDC 625
권호리스트/논문검색
제27권 제3호 통권131호 (2025년 6월) 13건
1.
2025.06
구독 인증기관 무료, 개인회원 유료
Blow-up in jointed concrete pavements refers to a type of distress caused by the excessive accumulation of compressive stress within concrete slabs, primarily resulting from internal expansion and elevated environmental temperatures. This phenomenon frequently leads to slab buckling and is challenging to predict in terms of both timing and location, thereby significantly threatening the long-term structural stability of the pavement. In the present study, the pavement growth and blow-up analysis (PGBA) model was employed to quantitatively predict the timing of blow-up events in jointed concrete pavements. The model estimates the maximum compressive stress within the slab throughout the pavement’s service life using input parameters such as reliability, climatic conditions, pavement structure, material properties, and expansion joint configurations. Subsequently, the model compares the estimated stress to the threshold stress associated with blow-up to determine the likely time of occurrence. A sensitivity analysis was performed on a range of design and environmental factors, including annual maximum temperature, annual maximum precipitation, coefficient of thermal expansion, ASR, pavement thickness, geometric imperfection, and expansion joint spacing and width. The influence of each factor on the predicted blow-up occurrence time was quantitatively evaluated. The analysis demonstrated that climatic conditions, pavement structure, material properties, and expansion joint characteristics, as considered in the PGBA model, collectively govern the timing of blow-up events. These findings offer critical insights for informing the design and maintenance strategies of jointed concrete pavements.
4,900원
2.
2025.06
구독 인증기관 무료, 개인회원 유료
This study aims to present a methodology and the corresponding results of an economic analysis, incorporating both costs and benefits, to assess the feasibility of introducing a smart on-board truck scale.The cost estimation was conducted based on direct expenditures associated with the installation and operation of smart on-board truck scales. The benefit analysis was performed by evaluating the reduction in social costs resulting from the mitigation of overloading, including transportation infrastructure maintenance costs, traffic accident costs, and environmental costs. The economic analysis outlines the variables required for each phase of the smart on-board truck scale implementation, along with their reasonable value ranges. In consideration of the uncertainty regarding the effectiveness of the smart on-board truck scales, a quantitative assessment of the impact of individual variables on the economic indicators was carried out through scenario analysis, focusing on key variables. The influence of the vehicle service life, the service life of the smart on-board truck scale, and personnel expenses—each related to installation and operation—on the benefit-cost ratio (B/C) and net present value (NPV) was determined to be limited. In contrast, the overload crackdown rate exhibited the most significant impact on the B/C and NPV, as it directly increased the number of vehicles contributing to measurable benefits. Notably, an increase in the discount rate led to a decrease in the values of both economic indicators. This outcome is expected, as the discount rate reduces the present value of future costs and benefits by increasing the denominator in the calculation. The introduction of smart on-board truck scales enables the achievement of economic feasibility in preemptive overload enforcement. Therefore, progressively expanding the number of vehicles equipped with smart on-board truck scales is essential to maximize their effectiveness in the near term.
4,000원
3.
2025.06
구독 인증기관 무료, 개인회원 유료
The rapid expansion of bridge and tunnel infrastructure has resulted in a growing incidence of wind-induced traffic accidents occurring at bridge approaches and tunnel portals. These accidents not only inflict direct damage on vehicles but also lead to substantial social and economic losses, stemming from roadway infrastructure repair and maintenance costs, as well as elevated logistics expenses due to traffic delays and congestion. In this study, a theoretical expression for the lateral displacement of vehicles as a function of wind speed was derived. Subsequently, lateral displacement and lateral wind force were analyzed and compared across vehicle types, considering both straight and curved roadway sections. An analysis of prevailing wind directions at each site revealed that, for passenger cars, the maximum lateral force and displacement on straight sections occurred at a wind incidence angle of 45°, whereas on curved sections with a pier curvature of 90°, the critical wind direction ranged from 0° to 120°. These results demonstrate that vehicle stability can be significantly compromised during high-speed travel under crosswind conditions. Based on departure trajectories of vehicles under varying wind speeds, a risk-assessment scale for wind-induced accidents was developed. In addition, design guidelines were proposed for the strategic placement of windbreak barriers to enhance driving safety under strong wind conditions.
4,000원
4.
2025.06
구독 인증기관 무료, 개인회원 유료
This study develops a comprehensive road operation evaluation model that integrates the perspectives of three principal stakeholders: road users prioritizing congestion mitigation, operators emphasizing investment efficiency, and policymakers advocating broader societal goals such as carbon reduction. The analysis database was constructed using traffic data obtained from reliable sources, including the Korea Transport Institute's Big Data Center and Suwon City's Urban Safety Integration Center. Binary logistic regression was employed to identify the factors influencing traffic congestion from the users’ perspective, whereas multiple linear regression models were used to analyze road investment efficiency from the operators’ viewpoint and carbon dioxide emissions from the policymakers’ standpoint. Statistical analyses were conducted on 4,322 road segments in Suwon City, with each evaluation criterion assigned an equal weight of 33.3 points in a unified 100-point scoring system. The analysis identified 15 statistically significant indicators affecting the three evaluation criteria, with the resulting models demonstrating strong explanatory power, evidenced by adjusted R² values of 0.197, 0.593, and 0.544 for traffic congestion, road investment efficiency, and carbon dioxide emission models, respectively. A volume-to-capacity (V/C) ratio of 0.64 was determined to represent the optimal balance point at which the requirements of all stakeholder groups align. When applied to Suwon City's arterial road network, the model identified 248 high-congestion segments (53.13 km), 203 segments with low investment efficiency (26.8 km), and 357 segments with high carbon emissions (156.33 km), each requiring targeted operational improvements. The proposed model addresses the limitations of existing single-stakeholder evaluation frameworks by offering transportation authorities a systematic and multi-dimensional approach to road operation assessment.
4,200원
5.
2025.06
구독 인증기관 무료, 개인회원 유료
South Korea has the highest suicide rate among the major OECD countries. The suicide rate in Seoul ranges from 21.3 to 23.2 per 100,000 individuals. To improve the survival rate of individuals attempting suicide on Han River bridges, the Seoul Fire Department began installing CCTVs on these bridges in 2012 and has been monitoring them to assess their effectiveness using collected data. This study aimed to evaluate the operational impact of deploying professional monitoring personnel and establishing an integrated video monitoring center by comparing suicide statistics before and after its implementation. The analysis distinguished between two periods: one when water rescue team members monitored the footage themselves after installing their own video monitoring center and the other when deploying professional monitoring personnel and establishing an integrated video monitoring center. After deploying professional monitoring personnel and operating the integrated video monitoring center, the number of rescue dispatches increased by an average of 50%, the survival rate improved by an average of 4.9%, and the mortality rate declined by an average of 4.9%.
4,000원
6.
2025.06
구독 인증기관 무료, 개인회원 유료
This paper presents a novel methodology for assessing the vulnerabilities of autonomous vehicles (AVs) across diverse operational design domains (ODDs) related to road transportation infrastructure, categorized by the level of service (LOS). Unlike previous studies that primarily focused on the technical performance of AVs, this study addressed the gap in understanding the impact of dynamic ODDs on driving safety under real-world traffic conditions. To overcome these limitations, we conducted a microscopic traffic simulation experiment on the Sangam autonomous mobility testbed in Seoul. This study systematically evaluated the driving vulnerability of AVs under various traffic conditions (LOSs A–E) across multiple ODD types, including signalized intersections, unsignalized intersections, roundabouts, and pedestrian crossings. A multivariate analysis of variance (MANOVA) was employed to quantify the discriminatory power of the evaluation indicators as the traffic volume was changed by ODD. Furthermore, an autonomous driving vulnerability score (ADVS) was proposed to conduct sensitivity analyses of the vulnerability of each ODD to autonomous driving. The findings indicate that different ODDs exhibit varying levels of sensitivity to autonomous driving vulnerabilities owing to changes in traffic volume. As the LOS deteriorates, driving vulnerability significantly increases for AV–bicycle interactions and AV right turns at both signalized and unsignalized intersections. These results are expected to be valuable for developing scenarios and evaluation systems to assess the driving capabilities of AVs.
4,500원
7.
2025.06
구독 인증기관 무료, 개인회원 유료
In this study, the effects of a hypothetical autonomous vehicle (AV)-exclusive roadway were estimated through a step-by-step approach using both microscopic and macroscopic simulations. First, the AV-exclusive roadway was classified into four types—entry lanes, mainlines, merging lanes, and intersections—and the C, α, and β values of the Bureau of Public Roads (BPR) function were estimated for each type through a microscopic simulation. These estimated values were then applied to a 3×3 (20 km) network, and a macroscopic simulation was conducted to compare the effectiveness of AVs and conventional vehicles (CVs) in terms of traffic volume and travel time.The analysis showed that for the same travel time, the traffic volume increased by more than 12% with AVs compared to that with CVs. Conversely, for the same traffic volume, the total travel time decreased by 11% for AVs. The estimated capacity of the AV-exclusive roadway, similar to the U-Smartway with a size of 3×3 (20 km), was approximately 400,000 vehicles, which was more than 140% higher than that of CVs. Assuming that each AV carries five passengers, up to two million people can be transported per day, indicating a significant potential benefit. However, these results were based on theoretical analyses using hypothetical networks under various assumptions. Future studies should incorporate more realistic conditions to further refine these estimations.
4,000원
8.
2025.06
구독 인증기관 무료, 개인회원 유료
Written examination for driver’s license certification plays a critical role in promoting road safety by assessing the applicants' understanding of traffic laws and safe driving practices. However, concerns have emerged regarding structural biases in multiple-choice question (MCQ) formats, such as disproportionate answer placement and leading linguistic cues, which may allow test-takers to guess the correct answers without substantive legal knowledge. To address these problems, this paper proposes a prompt-driven evaluation framework that integrates structural item analysis with response simulations using a large language model (LLM). First, we conducted a quantitative analysis of 1,000 items to assess formal biases in the answer positions and option lengths. Subsequently, GPT-based simulations were performed under four distinct prompt conditions: (1) safety-oriented reasoning without access to legal knowledge, (2) safety-oriented reasoning with random choices for knowledge-based questions, (3) performance-oriented reasoning using all available knowledge, and (4) a random-guessing baseline model to simulate non-inferential choice behavior. The results revealed notable variations in item difficulty and prompt sensitivity, particularly when safety-related keywords influence answer selection, irrespective of legal accuracy. The proposed framework enables a pretest diagnosis of potential biases in the MCQ design and provides a practical tool for enhancing the fairness and validity of traffic law assessments. By improving the quality control of item banks, this approach contributes to the development of more reliable knowledge-based testing systems that better support public road safety.
4,300원
9.
2025.06
구독 인증기관 무료, 개인회원 유료
Influential Factors of Personal Mobility Accident Severity Using a Cross-Classified Multilevel Model
With the rapid expansion of personal mobility (PM) devices as urban transport alternatives, the associated safety risks have increased significantly. Although previous studies have offered insights into user behavior and accident traits, more integrated approaches that consider spatial and administrative contexts are required to better understand the factors affecting accident severity. This study investigated the factors influencing accident severity involving PM devices in Seoul, South Korea by employing a cross-classified multilevel model (CCMM) to account for both police jurisdiction and regional characteristics. Analyzing the 2021 data from the Traffic Accident Analysis System (TAAS), the model showed strong validity (ICC: 15.8%, DIC: 697.2), outperforming the logistic and hierarchical models. Key predictors of higher severity included crashes in non-standard areas (e.g., other than single roads or intersections), helmet non-use, and older age of victims and perpetrators. Violations, such as exceeding passenger capacity, were negatively associated with severity. Industrial areas and high subway station densities reduced the severity, reflecting the benefits of pedestrian-friendly infrastructure. Larger areas covered by police officers significantly increased the severity, revealing enforcement limitations. The 2021 Road Traffic Act revision has had no statistically significant impact. These results highlight the need for integrated policies that combine infrastructure improvements, enhanced enforcement, and behavioral changes to reduce the severity of PM-related accidents in urban environments.
4,300원
10.
2025.06
구독 인증기관 무료, 개인회원 유료
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.
4,500원
11.
2025.06
구독 인증기관 무료, 개인회원 유료
This study analyzed the impact of improvements to the driver’s license system for elderly drivers on the incidence of traffic accidents. As South Korea’s population ages, the number of licensed drivers aged 65 years and older has surpassed 4.5 million as of 2024, accounting for approximately 15% of all license holders. Traffic accidents involving elderly drivers have increased steadily and tend to be more severe than those involving younger drivers. In response, the Road Traffic Act was amended in 2019 to shorten the license renewal cycle for drivers aged 75 and older, mandate dementia screening, and require traffic safety education. This study compared traffic accident statistics before and after the policy change (2018 and 2023) and used consulting data from 617 elderly drivers to examine the relationships between driving time, frequency, distance, and potential accident risk factors using a negative binomial regression analysis. The results show that after the policy changes, the number of traffic accidents per 10,000 elderly drivers decreased by up to 20.4%, demonstrating the effectiveness of the reforms. Furthermore, increased driving time, frequency, and distance were all significantly associated with a higher accident risk, whereas older age was linked to fewer accidents, likely owing to self-regulation among elderly drivers. Policy recommendations include limiting continuous driving time to 60 min, encouraging regular breaks, enhancing tailored safety education, tightening license aptitude test standards, and supporting the adoption of advanced safety features in vehicles. This study is expected to contribute to the development of effective policies to reduce traffic accidents among elderly drivers and create a safer traffic environment.
4,000원
12.
2025.06
구독 인증기관 무료, 개인회원 유료
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.
4,000원
13.
2025.06
구독 인증기관 무료, 개인회원 유료
Road Environment Suitability Evaluation of Autonomous Vehicles Using an Autonomous Driving Simulator
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.
4,300원
