간행물
한국도로학회논문집 KCI 등재 International journal of highway engineering
- 발행기관 한국도로학회
- 자료유형 학술지
- 간기 격월간
- ISSN 1738-7159 (Print)2287-3678 (Online)
- 수록기간 1999 ~ 2026
- 주제분류 공학 > 토목공학 공학 분류의 다른 간행물
- 십진분류KDC 534DDC 625
권호리스트/논문검색
제27권 제5호 통권133호 (2025년 10월) 13건
1.
2025.10
구독 인증기관 무료, 개인회원 유료
This study aims to validate the feasibility of using LiDAR reflectivity data to quantitatively estimate the retroreflectivity of road lane markings. The goal is to establish the optimal scanning conditions considering the channel position, angle of incidence, and vehicle speed for an accurate and consistent retroreflectivity assessment in mobile environments. Fifteen standard lane marking samples with known retroreflectivity values were scanned using an OS1-128 LiDAR sensor under controlled field conditions. A two-phase experiment was conducted: (1) a speed-based test to assess the influence of vehicle velocity (20-80 km/h) on LiDAR reflectivity measurements, and (2) a channel–angle–distance test using a static testbed to analyze the relationship between retroreflectivity, LiDAR channel position (that is, the angle of incidence), and measurement distance. Ground truth retroreflectivity values were obtained using a high-precision handheld retroreflectometer. Reflectivity measurements showed a strong correlation with standard retroreflectivity values, particularly at scanning angle between 100-115° and distances of 4.9-5.6 m. The coefficient of determination (R2) exceeded 0.97 across optimal conditions. Speedrelated tests confirmed that the LiDAR-based reflectivity remained stable with a minimal RMSE (< 5), even under high-speed driving scenarios. LiDAR sensors provided reliable and contactless estimates of pavement marking retroreflectivity when the channel angle and scanning distance were appropriately selected. The findings demonstrated that channel-specific calibration and incidence angle correction significantly improved the measurement accuracy. This suggests a practical path forward for automated large-scale retroreflectivity monitoring in road asset management systems.
4,000원
2.
2025.10
구독 인증기관 무료, 개인회원 유료
This study analyzes the impact of climate change on the performance of continuous reinforced concrete pavement (CRCP) and proposes a method to improve the existing KPRP–CRCP design procedure. Our analysis of monthly mean temperature data from the Seoul Meteorological Station revealed a general increase in temperature from 2001 to 2034, with a more significant increase observed during summer and winter. The existing KPRP–CRCP design method uses the drop temperature (DT) as a key variable. Notably, the increasing monthly mean temperatures owing to climate change tend to decrease the DT that in turn lowers the maximum stress on the pavement slab. This leads to a significant problem: if the traditional design method based on outdated data is used, the predicted number of punchouts will be lower than expected. This can result in an over-reduction in the reinforcement ratio and slab thickness, leading to premature failure and increased maintenance costs. To solve this issue, we introduced a predictive model for the final setting temperature that accounts for monthly and regional characteristics. Applying this model showed that as the temperature increased, the DT and maximum stress proportionally increased. This provided a more realistic prediction of the number of punchouts and addressed the flaws of the existing design method. Furthermore, our analysis of punchout counts based on the construction start month using this predictive model revealed that punchouts were more frequent in summer (July–August) and less frequent in winter (January–February). Based on this, we determined that the optimal seasons for placing continuous reinforced concrete pavements were spring (March–June) and fall (September–November). In situations where the actual construction start month was unknown, we recommended using a conservative design approach based on the design in August, when punchouts were most likely to occur.
4,000원
3.
2025.10
구독 인증기관 무료, 개인회원 유료
The purpose of this study is to explore the applicability of satellite-based synthetic aperture radar (SAR) data combined with pavement management system (PMS) indicators for effective road condition monitoring on mountainous local roads. Field survey data, including the International Roughness Index (IRI) and rutting measurements, were used as the ground truth, whereas Sentinel-1 and COSMO-SkyMed SAR images were processed using the time-series InSAR analysis to detect surface displacement and pavement deformations. In addition, a deep learning framework integrating PMS data and SAR imagery was developed, consisting of a swine transformer and CNN–LSTM networks for the classification and localization of pavement defects. The results demonstrated that X-band SAR backscatter values were correlated with IRI variations and that the proposed hybrid two-stage approach (CNN for surface damage and LSTM for rutting) enhanced the accuracy of defect detection compared with conventional single-model approaches. These findings highlight the potential of combining remote sensing and AI-based analysis with existing PMS datasets to provide a cost-effective and scalable solution for road asset management and maintenance prioritization.
4,000원
4.
2025.10
구독 인증기관 무료, 개인회원 유료
Permeable blocks are effective in improving urban water circulation and alleviating heat islands and floods. However, they cause environmental problems owing to their dependence on cement and natural aggregates. In this study, a permeable block was developed, and its performance was verified using ferronickel slag (FNS) as a substitute. The block applied with FNS met the KS F 4419 standard, and an average flexural strength of 4 MPa and a permeability coefficient of 0.1 mm/s or more were secured. This study confirmed that natural aggregate collection could be suppressed, resource efficiency could be improved, greenhouse gas reduction could be achieved, and high value-added industrial byproducts could be used.
4,000원
5.
2025.10
구독 인증기관 무료, 개인회원 유료
Autonomous vehicles are widely expected to be commercialized in the near future. This would naturally lead to situations in which existing vehicles and autonomous vehicles would be on the road at the same time, which would pose a notable hazard to traffic safety. From this perspective, high-risk factors relating to this deployment should be identified to prepare measures to promote traffic safety. However, at this point, deriving high-risk factors based on actual data is problematic because autonomous vehicles have not yet been widely commercialized. In this study, we derive high-risk factors that would apply if autonomous vehicles were allowed to drive alongside vehicles driven by humans using a meta-analysis. We synthesized factors related to autonomous vehicles mentioned in the relevant literature. An analysis was conducted based on a total of 58 documents according to five keywords related to autonomous vehicles (crash factors, scenarios, predictive models, laws, and regulations). We also performed a binary meta-analysis of factors related to autonomous vehicles according to these keywords and a meta-analysis of effect size according to the relative size of factors to evaluate them comprehensively. We found that many different aspects of driving such as navigating intersections, lanes, fog, rain, acceleration and deceleration, rear-end collisions, inter-vehicle spacing, and pedestrian collisions were notable as high-risk factors. This study provides basic data to identify high-risk factors to support the development of related prediction models.
4,200원
6.
2025.10
구독 인증기관 무료, 개인회원 유료
This study was conducted to analyze the amount of carbon dioxide generated by major types of traffic safety facility works during road construction, including traffic safety signs, median dividers, roadside guardrails, and gaze guidance facilities.The material and fuel input units for each detailed construction type were set based on the design data for road construction and top-level data, and the quantities were input and analyzed based on the design data. For the top-level data, civil engineering and design data provided by the Public Procurement Service were organized based on the general national road data and analyzed using SimaPro, a commercial program. A total of 300 [SI units] were generated for a 4.4 km section of the four-way road. Traffic safety signs were roughly 2.3 , guardrails were about 287.2 , and gaze guidance facilities were approximately 10.2 . Although the extension or quantity of each facility will vary depending on the characteristics of the design section, if the construction section is based on the basic unit, it can be determined that about 68.1 [SI units] of total carbon dioxide were generated, of which about 0.5 was generated for traffic safety signs, about 65 for guardrails, and about 2.3 was generated for gaze guidance facilities. In this study, we analyzed environmental factors that impact the construction of traffic safety facilities that were not specifically calculated in Korea. Moreover, our results can be used to judge the amount of carbon generated by road cycles in the future or to evaluate the qualities of carbon in terms of various types of traffic safety facilities.
4,600원
7.
2025.10
구독 인증기관 무료, 개인회원 유료
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.
4,000원
8.
2025.10
구독 인증기관 무료, 개인회원 유료
In the Autonomous Mobility Living Lab, traffic situations with both autonomous vehicles (AVs) and ordinary vehicles driven by humans (HDVs) are explored. Research on countermeasures and efficient transportation management plans has emerged from this context. In this study, we analyzed the effect of AVs with different speeds on signal intersections and road networks to derive efficient traffic operation plans for roads on which various AVs and HDVs with different driving behaviors are mixed in Living Lab cities. To that end, we conducted a simulation-based analysis of the effects of AV mixing rates on continuous signal intersections and the road network in traffic situations where AVs and HDVs were mixed at peak and non-peak driving hours. The simulation scenario was designed by classifying the traffic volume levels at peak and non-peak times and defining various AV mixing rates; we also set the driver behaviors of the AVs as either conservative or aggressive. By performing a small-scale traffic simulation, the average control delay, average stopped delay, average queue length, and average travel time of the signal intersection for each scenario were derived, and the impact of the AV mixing rate on traffic operation was analyzed. The results of the analysis show that higher AV mixing rates were associated with lower measurements of the effectiveness of signal intersections, which had a positive effect on traffic operation. This resulted in a stable and efficient improvement of the traffic flow at intersections as more vehicles passed through at the time of the allocated signal, as the AVs in the simulation could be driven at short intervehicle intervals by receiving real-time traffic information. In the traffic operation on the network, we found that the higher the AV mixing rate, the lower the average travel time, resulting in a greater effect of facilitating the traffic flow of the urban network. These simulated results indicate that higher AV mixing rates were associated with positive outcomes in terms of signal intersections and network traffic operation. We expect that this simulation can be used to establish real traffic operation plans in traffic situations where AVs are mixed at each stage of autonomous driving technology in the future.
4,000원
9.
2025.10
구독 인증기관 무료, 개인회원 유료
In this study, we investigated and analyzed the impact of changes in driving speed and inter-vehicle distance on users’ perceived tension during autonomous vehicle operation. To this end, a survey experiment was conducted for both urban roads and highways. The results show that the greatest changes in perceived tension occurred in the range of 50–70 Km/h and 50–70 m following distance on urban roads, and in the range of 80–100 Km/he and 60–80 m following distance on highways. Furthermore, modeling user behavioral responses to perceived tension based on changes in speed and following distance revealed that linear models best described the relationship for speed on both urban roads and highways. For the following distance, a quadratic model was the most suitable for urban roads, whereas a logarithmic model best fit the highway data. These findings are expected to contribute to practical operational guidelines for autonomous vehicles by alleviating users’ psychological discomfort and enhancing public acceptance. Future research will extend this study using a driving simulator to examine user responses in more realistic driving environments.
4,300원
10.
2025.10
구독 인증기관 무료, 개인회원 유료
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.
4,600원
11.
2025.10
구독 인증기관 무료, 개인회원 유료
With a view towards reducing traffic accidents on roadways, various methods have been considered to predict accidents. In this study, we analyze traffic accident frequency models that employ fixed- and random-parameter negative binomial approaches. Random parameters enable the inclusion of unobserved heterogeneity in traffic accident data, which current popular methods with fixed parameters such as Poisson or negative binomial models cannot consider in terms of time variation or segment-specific effects. A continuous, unbalanced panel of accident histories for 208 four-way signalized intersections on national highways in Seoul was used to estimate a traffic accident occurrence model that considered traffic volumes and various geometric characteristics at intersections. The results revealed that the left-turn exclusive lanes and traffic volumes on minor roads had random parameters that affected the likelihood of accident frequencies differently; the other variables were found to significantly affect traffic safety at the intersections on the national highways as fixed parameters. Based on these results, it can be concluded that the same traffic safety facilities have different effects on traffic accidents on major and minor roads. The insights from this study suggest the need for a broader analysis of integrated guidelines for facilities that impact intersection accident propensities.
4,000원
12.
2025.10
구독 인증기관 무료, 개인회원 유료
Accurate estimation of vehicle exhaust emissions at urban intersections is essential to assess environmental impacts and support sustainable traffic management. Traditional emission models often rely on aggregated traffic volumes or measures of average speed that fail to capture the dynamic behaviors of vehicles such as acceleration, deceleration, and idling. This study presents a methodology that leverages video data from smart intersections to estimate vehicle emissions at microscale and in real time. Using a CenterNet-based object detection and tracking framework, vehicle trajectories, speeds, and classifications were extracted with high precision. A structured preprocessing pipeline was applied to correct noise, missing frames, and classification inconsistencies to ensure reliable time-series inputs. Subsequently, a lightweight emission model integrating vehicle-specific coefficients was employed to estimate major pollutants including CO and NOx at a framelevel resolution. The proposed algorithm was validated using real-world video data from a smart intersection in Hwaseong, Korea, and the results indicated significant improvements in accuracy compared to conventional approaches based on average speed. In particular, the model reflected variations in emissions effectively under congested conditions and thus captured the elevated impact of frequent stopand- go patterns. Beyond technical performance, these results demonstrate that traffic video data, which have traditionally been limited to flow monitoring and safety analysis, can be extended to practical environmental evaluation. The proposed algorithm offers a scalable and cost-effective tool for urban air quality management, which enables policymakers and practitioners to link traffic operations with emission outcomes in a quantifiable manner.
4,200원
13.
2025.10
구독 인증기관 무료, 개인회원 유료
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.
4,000원
