Search Results (25)

Winter road icing poses significant safety risks, particularly on steep urban slopes with vulnerable populations. While thermal-comfort indices such as UTCI, PMV, and PET have been used for summer conditions, this study focuses on operational indices that quantify road-icing risk. This study introduces and empirically validates two novel indices—Freezing Intensity (FI) and Cold Intensity (CI)—designed to quantify the likelihood and severity of road icing. A case study was conducted on Namgyeong-maeul Road in Daedeok-gu, Daejeon, South Korea, where IoT-based environmental monitoring, including automated weather stations, thermal cameras, and drone imaging, was deployed from December 2024 to January 2025. Results demonstrate that road heating systems (RHS) effectively increased surface temperatures by an average of 4.1 °C compared to non-heated segments, with maximum differences exceeding 12.5 °C. The FI of non-heated slopes reached critical levels above 2400, whereas heated roads reduced FI to near zero. Similarly, CI values dropped from hazardous levels (~12) to below 6 in heated zones, reducing icing severity by more than 50%. These findings confirm that FI and CI can serve as robust metrics for operational assessment of RHS performance, complementing traditional heat-related indices. By integrating FI and CI into monitoring and design, policymakers and engineers can establish data-driven activation thresholds, optimize energy efficiency, and ensure safer winter mobility for vulnerable groups. This research provides a structured operational framework for winter road icing quantification, advancing climate adaptation strategies equivalent in rigor to summer climate indices. Compared with temperature-only monitoring, FI and CI improved operational responsiveness and reduced residual icing duration by ≈50%. Full article

In winter, some roads face the problems of severe rain accumulation and ice formation, which pose major risks to traffic safety and result in substantial economic losses. With the development of hydrophobic materials, hydrophobic coatings have gradually gained attention as a novel anti-icing technology. In this study, utilizing vinyl triethoxysilane (VTES) as the monomer and benzoyl peroxide (BPO) as the initiator, a hydrophobic anti-icing coating for highway pavements was prepared through the free radical polymerization method. Through designing the icing rate test and ice–pavement interface adhesion strength test, combining the contact angle test technology, wet wheel abrasion test, and pendulum friction coefficient test, the anti-icing performance, durability, and skid resistance performance of the hydrophobic anti-icing coating under the three types of mixtures of asphalt concrete (AC-13), Portland cement concrete (PCC), and porous asphalt concrete (PAC-13) were evaluated. The results indicate that when the surface layer of the pavement was sprayed with anti-icing coating, the water was dispersed in a semi-spherical shape and easily rolled off the road surface. Compared to uncoated substrates, the anti-icing coating reduced the icing rate on the surface by approximately 25%. Comparing with the uncoated pavements mixtures, for AC-13, PCC, and PAC-13 pavements, the ice–pavement interface adhesion strength after the application of hydrophobic anti-icing coating reduced by 30%, 79% and 34%, respectively. Both cement pavements and asphalt pavements, after the application of hydrophobic anti-icing coating, expressed hydrophobic properties (contact angle of 131.3° and 107.6°, respectively). After wet wheel abrasion tests, the skid resistance performance of pavement surfaces coated with the hydrophobic anti-icing coating met the specification requirements. This study has great significance for the promotion and application of hydrophobic anti-icing technology on highway pavements. Full article

Driving in snowy conditions challenges both human drivers and autonomous systems. Snowfall and ice accumulation impair vehicle control and affect driver perception and performance. Road markings are often obscured, forcing drivers to rely on intuition and memory to stay in their lane, which can lead to encroachment into adjacent lanes or sidewalks. Current lane detectors assist in lane keeping, but their performance is compromised by visual disturbances such as ice reflection, snowflake movement, fog, and snow cover. Furthermore, testing these systems with users on actual snowy roads involves risks to driver safety, equipment integrity, and ethical compliance. This study presents a low-cost virtual reality simulation for evaluating winter lane detection in controlled and safe conditions from a human-in-the-loop perspective. Participants drove in a simulated snowy scenario with and without the detector while quantitative and qualitative variables were monitored. Results showed a 49.9% reduction in unintentional lane departures with the detector and significantly improved user experience, as measured by the UEQ-S (p = 0.023, Cohen’s d = 0.72). Participants also reported higher perceived safety, situational awareness, and confidence. These findings highlight the potential of vision-based lane detection systems adapted to winter environments and demonstrate the value of immersive simulations for user-centered testing of ADASs. Full article

This study assesses the efficacy of the ZJWARMS model’s AI-based post-processing correction method for temperature and wind speed forecasts in complex terrain. By analyzing 72 h forecasts at four stations with varying elevations (from 273 m to 1327 m) in the Liuchun Lake region during December 2021–December 2022, the study found that AI-based corrections substantially enhanced both forecast accuracy and stability. The results indicate that, after correction, temperature forecast accuracy at all stations exceeded 99%, with the most notable relative gains at higher elevations (up to 48.1%). The mean absolute error (MAE) for temperature declined from 3.08 °C to below 0.8 °C at Octagonal Palace, and from 3.29 °C to below 0.6 °C at Mountaintop. Wind speed forecast accuracy also increased from approximately 60–70% to nearly 100%, with MAE generally constrained to the range of 0.2–0.4 m/s. In terms of extreme error control, the number of samples with temperature errors exceeding ±2 °C was markedly reduced. For instance, at Mountainside, the count dropped from 127 to 0. Extreme wind speed errors were also effectively eliminated. After correction, error distributions became more concentrated, and both temporal stability and spatial consistency showed notable improvement. These gains enhance operational forecasting and risk management in mountainous regions, for example, through threshold-based wind-hazard alerts and support for mountain-road icing, by providing more reliable, high-confidence guidance. Full article

Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead to more severe outcomes. Current regulations for post-crash fuel system integrity evaluation do not differentiate between EVs and ICE vehicles. However, the causes of fires in these vehicles differ due to variations in the design and construction of their fuel systems. This study analyzed seventeen cases of EV post-crash fires in Korea to derive two representative risk scenarios for EV post-crash fires. The first scenario involves significant intrusion into the EV front-end structure resulting from high-speed frontal collisions, while the second scenario involves direct impacts to the battery pack mounted under the vehicle from road curbs at low speeds (30–40 km/h). Based on these scenarios, we conducted tests to assess battery damage severity under two crash test modes, simulating both high-speed frontal collisions and low-speed curb impacts. The test results led to the development of a draft crash test concept to evaluate EV post-crash fire risks. Furthermore, we assessed the reproducibility of these test modes in relation to actual EV post-crash fires. Our findings indicate that square-shaped impactors provide higher reproducibility in simulating real EV post-crash fire incidents compared to hemisphere-shaped impactors. Additionally, a fire occurred 31 days after the storage of a crash-evaluated battery test specimen, which was determined to be caused by moisture invasion during post-crash storage, accelerating a micro-short circuit. This study aims to contribute to the development of new evaluation methods for the Korean New Car Assessment Program (KNCAP) to enhance EV post-crash fire safety by utilizing these test results to refine collision severity evaluation methods. Full article

While studies have typically examined the driving habits of young drivers living in large urban cities, few have examined the habits of young drivers living in smaller cities with large rural surrounding areas. Three surveys were disseminated to 193 young drivers, 65 police officers, and 62 driving instructors to examine the driving habits and challenging driving situations young drivers experience. Almost a fifth (18.1%) reported consuming alcohol prior to driving; alcohol consumption prior to driving was significantly associated with eating food/drinking beverages while driving, cellphone use, and speeding. The most challenging situations young drivers reported were night driving, encountering wild animals on the road, and driving in extreme weather conditions (e.g., ice, snow). Driving instructors reported that young drivers had challenges with lane positioning, speed control, and navigating traffic signs and signals. Additionally, police officers reported issuing tickets to young drivers primarily for failure to stop, distracted driving, impaired driving, and speeding. Young drivers living in smaller cities and rural communities have unique challenges, including interactions with wildlife, driving on gravel roads, and driving in poor weather and road conditions (e.g., ice, snow). Opportunities for young drivers to be exposed to these scenarios during driver training are critical for increasing awareness of these conditions and reducing crash risk. Full article

Winter weather can cause extremely dangerous road conditions. In order to analyze traffic accidents occurring in winter months in more detail, it is very important to evaluate the slope and the condition of road surfaces together. For this purpose, this study analyzed the accidents that occur during these months in Erzurum, one of the cities in Turkey with long winter months. A total of nine different classes of road conditions were created according to these two factors. In accordance with these classes, the accidents were analyzed using machine learning algorithms, and the success of the classification was analyzed. As a result of the analysis, it was found that the J48 algorithm gave more accurate results. J48 processes both continuous and categorical attributes and is a decision tree algorithm that can effectively manage missing data. According to the results of this algorithm, a map of accident density in the city was created using ArcGIS 10.5 software. Accordingly, it was found that the highest risk of accidents during the winter months occurred on road sections with a slope of more than 6% and covered with ice. Another important result of the study is that the slope of the road is a more effective factor than the surface condition. Full article

Urban greening is threatened by the concern that street trees increase traffic-related injury/death. Associations between all serious and fatal traffic crashes and street tree percentages were examined in Sydney, Australia. Associations were adjusted for confounding factors relating to driver behavior (speeding, fatigue, and use of alcohol) and road infrastructure, including alignment (e.g., straight, curved), surface condition (e.g., dry, wet, ice), type (e.g., freeway, roundabout), and speed limit. Models indicated that 10% more street trees were associated with 3% and 20% higher odds of serious or fatal injuries and 20% tree collisions on roads of any speed, respectively. However, further analysis stratified by speed limit revealed contrasting results. Along roads of 70 km/h or greater, 10% more street trees were associated with 8% higher odds of serious or fatal injury and 25% higher odds of death. Comparable associations were not found between street trees and serious or fatal injuries along roads below 70 km/h. Reducing speed limits below 70 km/h saves lives and may mitigate risks of serious or fatal traffic accidents associated with street trees, enabling greener, cooler, healthier cities. Full article

The black soil region of Northeast China is the largest commercial grain production base in China, accounting for about 25% of the total in China. In this region, the water erosion is prominent, which seriously threatens China’s food security. It is of great significance to effectively identify the erosion-prone points for the prevention and control of soil erosion on the slope of the black soil region in Northeast China. This article takes the Tongshuang small watershed (Heilongjiang Province in China) as an example, which is dominated by hilly landforms with mainly black soil and terraces planted with corn and soybeans. Based on the 2.5 cm resolution Digital Elevation Model (DEM) reconstructed by unmanned aerial vehicles (UAVs), we explore the optimal resolution for hydrological simulation research on sloping farmland in the black soil region of Northeast China and explore the critical water depth at which erosion damage occurs in ridges on this basis. The results show that the following: (1) Compared with the 2 m resolution DEM, the interpretation accuracy of field roads, wasteland, damaged points, ridges and cultivated land at the 0.2 m resolution is increased by 4.55–27.94%, which is the best resolution in the study region. (2) When the water depth is between 0.335 and 0.359 m, there is a potential erosion risk of ridges. When the average water depth per unit length is between 0.0040 and 0.0045, the ridge is in the critical range for its breaking, and when the average water depth per unit length is less than the critical range, ridge erosion damage occurs. (3) When local erosion damage occurs, the connectivity will change abruptly, and the remarkable change in the index of connectivity (IC) can provide a reference for predicting erosion damage. Full article

In efforts to fulfill the objectives of taking part in pragmatic cooperation in the Arctic, constructing the “Silk Road on Ice”, and ensuring ships’ safety and risk assessment in the Arctic, the two biggest hazards, which concern ships’ navigation in the Arctic, are wind and sea ice. Sea ice can result in a ship being besieged or crashing into an iceberg, endangering both human and property safety. Meanwhile, light winds can assist ships in breaking free of a sea-ice siege, whereas strong winds can hinder ships’ navigation. In this work, we first calculated the spatial and temporal characteristics of a number of indicators, including Arctic wind speed, sea-ice density, the frequency of different wind directions, the frequency of a sea-ice density of less than 20%, the frequency of strong winds of force six or above, etc. Using the ERA5 wind field and the SSMI/S sea-ice data, and applying statistical techniques, we then conducted a joint analysis to determine the correlation coefficients between the frequencies of various wind directions, the frequency of strong winds and its impact on the density of sea ice, the frequency of a sea-ice concentration (SIC) of less than 20%, and the correlation coefficient between winds and sea-ice density. In doing so, we determined importance of factoring the wind’s contribution into sea-ice analysis. Full article

The aberrant winter temperatures resulting from climatic shifts give rise to the formation of imperceptible black ice on road surfaces, posing a risk of accidents. In this study, a carbon nanotube (CNT)-based heating module was fabricated, embedded in a concrete slab, and subjected to a full-scale test in an outdoor environment. Preliminary tests were conducted to scrutinize the thermal behavior of the CNT heating modules applied to the concrete slab, considering the inter-module distance and the concentration of multiwalled carbon nanotubes (MWCNTs) in the concrete perimeter. A full-scale concrete slab was fabricated on the basis of the preliminary test results. Thermal performance analyses of the concrete perimeter were performed according to the MWCNT concentration, the distance between the MWCNT heating modules, and the supply voltage based on a full-scale test conducted in an outdoor environment. The full-scale test results indicated that the maximum temperature variation of the MWCNT heating module embedded concrete slab was 46.8 °C, and its thermal performance varied by 1.9 times depending on the concentration of MWCNTs in the concrete perimeter. Full article

With the development of new technologies inside car mechanisms with various sensors connected to the IoT, a new generation of automation is attracting attention. However, there are still some factors that are difficult to detect. Among them, one of the highest risk factors is black ice. A road covered with black ice, which is hard to see from a distance, is not only the cause of damage to vehicles passing over the spot, but it also puts lives at risk. Hence, the detection of black ice is essential. A lot of research has been done on this topic with various sensors and methods. However, hyperspectral imaging has not been used for this particular purpose. Therefore, in this paper, black ice classification has been performed with the help of hyperspectral imaging in collaboration with a deep learning model for the first time. With abundant spectral and spatial information, hyperspectral imaging is a good way to analyze any material. In this paper, a 2D–3D Convolutional Neural Network (CNN) has been used to classify hyperspectral images of black ice. The spectral data were preprocessed, and the dimension of the image cube was reduced with the help of Principal Component Analysis (PCA). The proposed method was then compared with the existing method for better evaluation. Full article

Snow and ice forming on the entrance and exit driveways of underground car parks of buildings brings serious difficulties and risks in safe parking for vehicles in winter. Even though traditional methods such as chemical salt and snow plowing reduce slippery conditions on driveways, they also result in infrastructure- and environment-related damages. Hydronic heating is an alternative way to prevent snow and ice forming; thereby, the hydronic heating driveway (HHD) is a promising technique for energy-efficient and environment-friendly solutions. This study presents a time-dependent three-dimensional numerical heat transfer model for HHD applications with realistic boundary conditions and meteorological data in the MATLAB environment. After developing the numerical heat transfer model, the model is applied to a case study in Istanbul, Turkey and followed by an economic comparison with the commercial electrically-heated driveways (EHD) method that is applied in two different ways; applying the electric cables in (i) whole driveway and (ii) only tire tracks. Different escalation rates in natural gas and electricity, hot fluid inlet temperature, air temperature, and the number of parallel pipes are the main parameters in the case study. Results show that the decrease in pipe spacing drops the investment cost term but it needs a higher supplied fluid temperature for anti-icing, and therefore the operating cost term increases. Among other cases was the number of parallel pipes, with 50 being the most economically feasible solution for all air temperatures ranging from 0 °C to −10 °C. The economic comparison shows that the EHD with only tire tracks has the minimum total cost as it significantly decreased both the operating and investment cost terms. In case of an anti-icing requirement on the whole road surface, the HHD system was found to be preferable to the EHD whole driveway scenario at air temperatures of 0 °C and −5 °C, while it is more beneficial only for the high electricity escalation rates at the ambient temperature of −10 °C. Full article

The traditional method of removing ice and snow on roads carries the risk of damaging roads and the environment. In this circumstance, the technology of salt-storage asphalt pavement has gradually attracted attention. However, snow-melting salts may also have an impact on asphalt mixture performance. To explore the effect of snow-melting salts on the mechanical and surface properties of salt-storage asphalt mixtures (SSAM), SSAMs were prepared with styrene–butadiene–styrene (SBS)-modified asphalt and high-elastic asphalt (HEA) as binders and snow-melting salts as fillers. The influence of the type of asphalt binder and the content of snow-melting salt on the performance of the SSAM was preliminarily investigated through laboratory tests. The results show that the high-temperature, low-temperature, and moisture resistance performance of the SBS group SSAM decreased by 9.8–15.1%, 1.6–12.3%, and 6.3–19.4%, respectively, compared with SBS00. The higher the amount of snow-melting salt, the greater the performance drop. The three mechanical properties of the HEA group containing high-elastic agent TPS are 11.3–19.7%, 4.2–12.3%, and 4.8–13.3% higher than that of the SBS group. Even when the content of snow-melting salt is 50% or 75%, the mechanical properties of the HEA group are better than that of SBS00 without snow-melting salt. Snow-melting salt has clear advantages in improving the anti-skid performance but decreases the anti-spalling performance. The surface properties of the HEA group were also better than that of the SBS group. Considering the mechanical properties and surface properties, the comprehensive performance of the HEA group is better than that of the SBS group, and HEA50 has the best comprehensive performance. In addition, the construction performance of the SSAM has also been verified, and the production of SSAM according to the hot mix asphalt can meet the specification requirements. Full article

As the development of mountain areas has recently increased in Korea, existing roads are being renovated, and new highways are being constructed, which increases driving speeds in mountainous areas. However, the mountainous region in northeastern Korea is more likely to form black ice due to higher humidity, frequent fog, and hillshade, depending on the terrain, which can cause serious traffic pileups. In this study, therefore, we present a method to build a more effective black ice prediction and warning system by linking spatial information to the existing road management system that estimates the road surface temperature based on real-time weather information. The spatial information enabled a prediction to be made of the risk level of black ice formation for each time zone by simulating changes in the shadow area based on precise 3D terrain information. Moreover, this information also presented slope and curvature information of the road to estimate the risk zone. The spatial information was integrated with weather data to predict road surface temperature. The proposed system was tested in two mountainous regions with weather data accumulated from 2017 to 2018. As a result, the proposed system anticipated 71% of traffic accidents caused by black ice during the testing period. The results show that the system can contribute significantly to preventing black-ice-related traffic accidents by providing reasonable predictions. Full article