• International Journal of Highway Engineering
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• v.17 no.2
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• pp.143-150
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• 2015

PURPOSES : The authors set out to estimate the related carbon emissions, energy use, and costs of the national freeways and highways in Korea. To achieve this goal, a macro-level methodology for estimating those amounts by road type, road structure type, and road life cycle was developed. METHODS : The carbon emissions, energy use, and costs associated with roads vary according to the road type, road structure type, and road life cycle. Therefore, in this study, the road type, road structure type, and road life cycle were classified into two or three categories based on criteria determined by the authors. The unit amounts of carbon emissions and energy use per unit road length by classification were estimated using data gathered from actual road samples. The unit amounts of cost per unit road length by classification were acquired from the standard cost values provided in the 2013 road business manual. The total carbon emissions, energy use, and cost of the national freeways and highways were calculated by multiplying the road length by the corresponding unit amounts. RESULTS: The total carbon emissions, energy use, and costs associated with the national freeways and highways in Korea were estimated by applying the estimated unit amounts and the developed method. CONCLUSIONS: The developed method can be employed in the road planning and design stage when decision makers need to consider the impact of road construction from an environmental and economic point of view.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.112-119
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• 2005

In road tunnel, in order to prevents an accident and minimize the damage of an accident in the case of fire, safety facilities and equipments are integral parts. The type and amount of safety facilities are based on tunnel type and length, traffic flow rate, etc. Therefore many countries use a tunnel classification system that categories tunnel into groups, and specifies the necessary emergency equipment for each group. In this study, for the purpose of classifying tunnel based on tunnel ist investigated the domestic and foreign standards and regulations for safety of road tunnel. As a results, we suggest the method of classification of tunnel by traffic performance, tunnel grade, the volume of traffic, fraction of HGV, rules or regulations for transports of dangerous good through tunnel.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.313-323
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• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.582-590
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• 2015

Road type classification and K-factors are important role when design of number of lane. In this study not only classifies road type and estimating of K-factor but also economic evaluation tries for feasibility verification. Road type analysis results, time of day traffic volume variation, weekend-factor and vacation-factor are large in recreation roads. Weekday traffic volume and weekend traffic volume are similar patterns in provincial roads. AADT is high and time of day traffic volume variation is small in urban roads. In this study compares with economic analysis that designing of number of lane between KHCM's K-factor and this study K-factor. Economic analysis results, designed roads by this study's K-factor reduce cost about 4,708 hundred million won. So this study's K-factor is economical on provincial 4 lane roads.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.143-151
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• 2017

Recently Korea has presented carbon emission reduce goal of 37% compare to BAU until 2030 according to Paris Agreement in order to correspond to climate change. For this, researchers need to study positively on construction industry that emit $CO_2$ of $3^{rd}$ volume of 28 industry classification. This study calculated environmental load by LCA using the road part except tunnel and bridge among national road cases completed already. After selecting representative type of large construction type based on environmental emission, earth works, drainage works and paving works took up 84%. And this study analyzed the environmental emission feature of each detail construction type after selecting representative type each detail construction type. Utilization of each construction type emission attribute to environmental load during national road construction, will be helpful in making decision of eco-friendly national road construction based on environmental emission.

• Journal of Environmental Science International
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• v.23 no.7
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• pp.1349-1358
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• 2014

This study suggests plan of green space management based on the result of research apprehending the characteristic through sorting types of city thermal environment targeting summer which thermal pollution is the most serious. Considering anthropogenic heat, development level of wind road, thermal environment, as a result of types of thermal environment process, it is appeared 36 types, and 10 types is relevant of this research subject. Type I-1, size of building is large, artificial covering area is wide, and thermal load of anthropogenic heat is high, type II-1, development condition of wind road is incomplete as IIlevel, entering cold air is difficult and thermal management and improvement is needed area. Type III-1, scale is large and it is area of origin of cold air, development level of wind road is mostly favorable, type III-2 is revealed as smaller scale than III-1, and small area of origin of cold air. Type IV, anthropogenic heat is $81{\sim}150W/m^2$, average, but development function of wind road is very favorable. Type V, large area of thermal load and the origin of cold air are distributed as similar ratio, and level of development function of wind road is revealed as II level. According to standard of type classification of thermal environment, as a result of suggesting plan of green space management and biotops area ratio, type I-1 is buffer green space and waterway creation, goal biotops area ratio 35%, type II-1 afforestation in site and goal biotops area ratio 40%, type III-1, preservation plan to display the current function continuously is requested. Type IV suggests afforestation of stream current, and type V suggests quantitative increase of green space and goal biotops area ratio 45%.