• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.233-248
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• 2012

The national emission from energy sector accounted for 84.7% of all domestic emissions in 2007. Of the energy-use emissions, the emission from mobile source as one of key categories accounted for 19.4% and further the road transport emission occupied the most dominant portion in the category. The road transport emissions can be estimated on the basis of either the fuel consumed (Tier 1) or the distance travelled by the vehicle types and road types (higher Tiers). The latter approach must be suitable for simultaneously estimating $CO_2$, $CH_4$, and $N_2O$ emissions in local administrative districts. The objective of this study was to estimate 31 municipal GHG emissions from road transportation in Gyeonggi Province, Korea. In 2008, the municipalities were consisted of 2,014 towns expressed as Dong and Ri, the smallest administrative district unit. Since mobile sources are moving across other city and province borders, the emission estimated by fuel sold is in fact impossible to ensure consistency between neighbouring cities and provinces. On the other hand, the emission estimated by distance travelled is also impossible to acquire key activity data such as traffic volume, vehicle type and model, and road type in small towns. To solve the problem, we applied a hierarchical cluster analysis to separate town-by-town road patterns (clusters) based on a priori activity information including traffic volume, population, area, and branch road length obtained from small 151 towns. After identifying 10 road patterns, a rule building expert system was developed by visual basic application (VBA) to assort various unknown road patterns into one of 10 known patterns. The expert system was self-verified with original reference information and then objects in each homogeneous pattern were used to regress traffic volume based on the variables of population, area, and branch road length. The program was then applied to assign all the unknown towns into a known pattern and to automatically estimate traffic volumes by regression equations for each town. Further VKT (vehicle kilometer travelled) for each vehicle type in each town was calculated to be mapped by GIS (geological information system) and road transport emission on the corresponding road section was estimated by multiplying emission factors for each vehicle type. Finally all emissions from local branch roads in Gyeonggi Province could be estimated by summing up emissions from 1,902 towns where road information was registered. As a result of the study, the GHG average emission rate by the branch road transport was 6,101 kilotons of $CO_2$ equivalent per year (kt-$CO_2$ Eq/yr) and the total emissions from both main and branch roads was 24,152 kt-$CO_2$ Eq/yr in Gyeonggi Province. The ratio of branch roads emission to the total was 0.28 in 2008.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.89-101
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• 2016

This study examined spatial disposition of wildlife highway mortality using road-kill GIS database and Naver panoramic 360 degree views to find out which habitat and road variables most influenced road-kill numbers for each mammal species and how the landscape and road elements are connected on highway. Road-kills on Yeongdong(YD) and Jungbu highway(JB) generally tended to be higher in natural barren, grassland and cropland due to its value of preferred habitats of nocturnal and multihabitat species like water deer(Hydropotes inermis argyropus), raccoon(Nyctereutes procyonoides) and hare(Lepus coreanus). Land cover in YD showed no difference between species (p=0.165) while JB did by species (p=0.001). This may be explained by disparate landscape between mountain and urban or the fact that YD in long term operation might have enabled consistent crossing pattern compared to JB experiencing continuous extension works which may in turn have deviated the road crossing. Although road-kill prevention effect of local topography alone was appreciable, compared to less significant or ineffective fence and guardrail, gentle slope declining in a direction to the road turned out to offset the preventive effect of juxtaposed fence. Furthermore, green patches on road near intersection were deemed a visual stepping stone facilitating wildlife attempted crossing and local roads juxtaposed with a highway were especially left defenceless to road-kill without road barriers.

• Particle and aerosol research
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• v.8 no.3
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• pp.111-120
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• 2012

In Daegu, a road cleaning system was constructed in the central part of the city and has been operated from April, 2011. We evaluated the effect of the system on the concentration of $PM_{10}$ at a roadside monitoring site. The ambient $PM_{10}$ concentration data were logged every 1 min for a period of 20 weeks from May to October, 2011, by means of light scattering method, and then every 5 min data were used in the statistical analysis. The measured data were verified by comparing them with beta-ray data obtained at the same site. Correlation coefficient between the two groups was highly significant (r=0.79), though the absolute levels of light scattering data appeared to be approximately 2.8 times higher than the beta-ray data. Diurnal, daily, weekly, and monthly variations of $PM_{10}$ data did not show any evidence of decreasing effect owing to the clean road system. A comparison of roadside $PM_{10}$ data with non-roadside data also revealed very similar pattern, implying the variation of the $PM_{10}$ concentrations is mainly affected by the traffic conditions near the monitoring site. However, if the operating conditions of the clean road system can be improved, i.e. increasing the frequency and duration of water cleaning, the road cleaning effect may improve the air quality indirectly by means of removing the resuspended particles from the road.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.221-228
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• 2010

A sliding accident on the road have a high percentage by road freezing, especially, it is often appeared at bridges and Tunnel of freezing areas. Thus, the stability of road operations is enhanced by preventing a partial freezing phenomenon. According to the geothermal snow melting system analysis, a pattern of thermal conductivity is found out about pavement materials of concrete and asphalt when it is buried. The thermal conductivity study is essential that be applied the geothermal snow melting system according to heating exchanger pipe laying of lower pavements. The model tests are conducted on low temperature in freezer using the manufactured test model which is equal to pavement materials. And Many variables are discovered from numerical analyzes of the same conditions with model test.

• Journal of the Korean GEO-environmental Society
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• v.18 no.1
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• pp.49-58
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• 2017

Due to low annual average temperature, road tunnel lining in domestic cold region (Gangwon province) experiences durability problems. The financial and human damage due to cracks, breakout, exfoliation and water leakage increases every year. However, domestic research on effect of temperature on road tunnel lining damage is insufficient. Thus, this research has investigated 70 tunnels located in cold region (Gangwon-do) to analyze damage status. Furthermore, by contrasting damage on tunnels in relatively warm Gangneung area with those in relatively cold Hongcheon area, the effect of temperature on road tunnel lining damage was analyzed.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• International Journal of Automotive Technology
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• v.4 no.2
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.107-116
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• 2014

This paper presents camera-based road surface marking detection methods suited to sensor fusion-based positioning system that consists of low-cost GPS (Global Positioning System), INS (Inertial Navigation System), EDM (Extended Digital Map), and vision system. The proposed vision system consists of two parts: lane marking detection and RSM (Road Surface Marking) detection. The lane marking detection provides ROIs (Region of Interest) that are highly likely to contain RSM. The RSM detection generates candidates in the regions and classifies their types. The proposed system focuses on detecting RSM without false detections and performing real time operation. In order to ensure real time operation, the gating varies for lane marking detection and changes detection methods according to the FSM (Finite State Machine) about the driving situation. Also, a single template matching is used to extract features for both lane marking detection and RSM detection, and it is efficiently implemented by horizontal integral image. Further, multiple step verification is performed to minimize false detections.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.139-149
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• 2004

As the expansion of road capacity has become impractical in many urban areas, congestion pricing has been widely considered as an effective method to reduce urban traffic congestion in recent years. The principal reason is that the congestion pricing may lead the user equilibrium (UE) flow pattern to system optimum (SO) pattern in road network. In the context of network equilibrium, the link tolls according to the marginal cost pricing principle can user an UE flow to a SO pattern. Thus, the pricing method offers an efficient tool for moving toward system optimal traffic conditions on the network. This paper proposes a continuous network design program (CNDP) in network equilibrium condition, in order to find optimal congestion toll for maximizing net economic benefit (NEB). The model could be formulated as a bi-level program with continuous variable(congestion toll) such that the upper level problem is for maximizing the NEB in elastic demand, while the lower level is for describing route choice of road users. The bi-level CNDP is intrinsically nonlinear, non-convex, and hence it might be difficult to solve. So, we suggest a heuristic solution algorithm, which adopt derivative information of link flow with respect to design parameter, or congestion toll. Two example networks are used for test of the model proposed in the paper.