• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.90-105
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• 2013

There are lots of variations on speed, acceleration and engine power during vehicle driving. It is well known that Green House Gas emissions by these dynamic driving properties are not precisely estimated by the average speed based emission estimation model which has been currently used in Korea. MOVES are selected as an appropriate transferable model among Micro-level emission estimation models. Based on MOVES, a novel emission estimation model can be used in Korea is developed. In this model, MOVES concept of emission estimation method and the MOVES method of estimating the Micro-level emission rate map is adopted. The results from the proposed model were compared with those from the average speed based emission model. The comparison results show the estimated base emission maps are good to be applied in Korea, but needed to be adjusted to consider the vehicle size differences between the two countries. Therefore, the factors for calibrating vehicle size difference were calculated and applied to acquired the micro-level emission maps for the Korean standard vehicle types.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.449-460
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• 2015

The reliability of air quality evaluation results for green transportation could be improved by applying correct emission factors. Unlike previous studies, which estimated emission factors that focused on vehicles in laboratory experiments, this study investigates emission factors according to road types and time using real driving data. The real driving data was collected using a Portable Activity Monitoring System (PAMS) according to road types and time, which it compared and analyzed fuel consumption from collected data. The result of the study shows that fuel consumption on national highway is 17.33% higher than the fuel consumption on expressway. In addition, the average fuel consumption of peak time is 4.7% higher than that of non-peak time for 22.5km/h. The difference in fuel consumption for road types and time is verified using ANOCOVA and MANOVA. As a result, the hypothesis of this study - that fuel consumption differs according to road types and time, even if the travel speed is the same - has proved valid. It also suggests correction factor of emission factors by using the difference in fuel consumption. It is highly expected that this study can improve the reliability of emissions from mobile pollution sources.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.180-190
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• 2016

Atmospheric pollutants such as Nitrogen Oxides(NOx), Carbon Monoxide(CO), Carbon Dioxide($CO_2$), Particulate Matter(PM) and Hydrocarbons(HC) come from vehicle exhaust gases. Emission curves based on average travel speeds have been employed for estimating on-road emissions as well as evaluating environmental impacts of transportation plans and policies in Korea. Recently, there is a growing interest in estimation methods of vehicle emissions considering relationship between vehicle dynamic driving characteristics and emissions, and incorporating such emission estimators into traffic simulation models. MOVES Lite, a simplified version of MOVES, is one of the estimation methods. In this study, the authors performed a study to develop an adaptable version of MOVES Lite for Korea, called MOVES Lite-K. Vehicle types, driving characteristics, emission rates, and emission standards of Korea were reflected in MOVES Lite-K. The characteristics of emission calculation of MOVES Lite-K and NIER emission curves were compared and the adaptability of MOVES Lite-K were examined.

• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.560-569
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• 2019

Purpose: Greenhouse gases are one of the major causes of global warming, a global disaster. This study aims to calculate road sector greenhouse gas emissions more precisely than conventional methods. Method: Currently, the average speed of a vehicle is used to calculate greenhouse gas emissions. In this study, GHG emissions are calculated using the speed of individual vehicles and compared with current methods. Result: It was confirmed that the existing emission estimation method underestimated about 15% in the case of carbon dioxide, about 1% in the case of nitrous oxide, and about 1% in the case of methane. Conclusion: Current methods of estimating greenhouse gas emissions were developed before 2000 and were developed to meet the limits of available data. However, with the advancement of technology, the quality of available data is now high, and new emissions estimation methods are needed. Therefore, in this study, we propose a method for estimating the velocity-based greenhouse gas emissions of individual vehicles as a more accurate method for calculating greenhouse gas emissions.