• Spatial Information Research
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• v.22 no.1
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• pp.9-17
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• 2014

This study was intended to reliably predict the traffic green house gas emission in Daegu with the use of spatial statistical technique and calculate the traffic green house gas emission of each administrative district on the basis of the accurately predicted emission. First, with the use of the traffic actually surveyed at a traffic observation point, and traffic green house gas emission was calculated. Secondly, on the basis of the calculation, and with the use of Universal Kriging technique, this researcher set a suitable variogram modeling to accurately and reliably predict the green house gas emission at non-observation point suitable through spatial correlation, and then performed cross validation to prove the validity of the proper variogram modeling and Kriging technique. Thirdly, with the use of the validated kriging technique, traffic green gas emission was visualized, and its distribution features were analyzed to predict and calculate the traffic green house gas emission of each administrative district. As a result, regarding the traffic green house gas emission of each administration, it was found that Bukgu had the highest green house gas emission of $291,878,020kgCO_2eq/yr$.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.114-128
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• 2013

Greenhouse gas emissions have been an important issue in different countries because of their effects on global warming. The government has to organize greenhouse gas reduction measures suitable to regional characteristics by establishing annual implementation plans and comprehensive policies based on the UNFCCC. The transportation sector is one of the major contributors of air pollution; hence increasing need to estimate current and future traffic emissions precisely. Under these circumstances, a number of emission models have been developed recently. However, current methods of estimation cannot carry out effective analyses because it does not reflect vehicle movement characteristics. This study aims to present a new method for calculating road traffic emissions in Goyang city. A travel demand model is utilized to carry out GHG emission estimates according the traffic data (fleet composition, vehicle kilometers travelled, traffic intensity, road type, emission factors and speed). This study evaluates two approaches to estimate the road traffic emissions in Goyang City: Pollution-Emis and the Handbook of Emission Factors for Road Transport (HBEFA v.3.1) which is representative of the "average speed" and the "traffic situation" model types. The evaluation of results shows that the proposed emission estimation method may be a good practice if vigilant implementation of model inputs is observed.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.117-126
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• 2012

The transportation sector accounts for 33% of the total $CO_2$ emissions. Effective control measures for reducing $CO_2$ emissions are urgently needed to address global warming. Objective and reliable methods to estimate $CO_2$ emissions are a prerequisite for the implementation of such effective control measures. However, existing models have not been successful. Even though the average-speed model is one of the most convenient and useful methods for estimating $CO_2$ emissions, it cannot distinguish between a variety of roads as well as traffic conditions in the model. The results of this study found that there may be significant discrepancies between emissions estimated by the current average-speed model and those measured in real driving conditions. This paper proposed the subdivision of emission factors in the average-speed model depending on both traffic and road conditions.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.694-702
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• 2014

Since Greenhouse Gas Inventory & Research Center (GIR) of Korea was founded in 2010, the annual greenhouse gas inventory reports, one of the collections of GIR's major affairs, have been published from 2012. In the reports many items related to greenhouse gas emission quantities are included, but among them uncertainty values are replaced to basic values which IPCC guideline suggests. Even though IPCC guideline suggests the equations of each Tier level in details, the guideline recommends developing nation's own methodology on uncertainty which is closely related to statistical problems such as the estimation of a probability density function or Monte carlo methods. In the road transportation sector the emissions have been calculated by Tier 1 but the uncertainties have not been reported. This study introduce a bootstrap technique and Monte carlo method to estimates annual emission quantity and uncertainty, given activity data and emission factors such as annual traveled distances, fuel efficiencies and emission coefficients.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.6
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• pp.94-104
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• 2010

The capacity of highway is restricted and traffic congestion is caused by increasing traffic demand. Also, greenhouse gases are increased by traffic congestion. CDM (Clean Development Mechanism) is an idea of interest to reduce greenhouse gases. However, CDM's cases applied in traffic field are rare. Thus, it is necessary that methodology to reduce greenhouse gas should be developed and applied to CDM. A methodology for identifying greenhouse gas emissions was developed in this paper. This methodology was developed on the basis of baseline methodology registered at UN. Travel time and speed in the conventional traffic condition and in the automated traffic condition are compared by BPR function. The calculated speed applied to emission factor equation and then $CO_2$ emissions was calculated. A simulation was executed to evaluate the validity of the developed methodology. In the result, advanced vehicle's $CO_2$ emissions are more than conventional vehicle's $CO_2$ emissions in the stable flow condition. However, advanced vehicle's $CO_2$ emissions are less than conventional vehicle's $CO_2$ emissions in the unstable flow condition. It is assure that capacity of highway is enhanced and efficiency of highway is improved by adopting advanced safety vehicle in the smart road.

• Transportation Technology and Policy
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• v.7 no.5
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• pp.81-89
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• 2010

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.141-148
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• 2022

Global climate change caused by greenhouse gases(GHG) is getting serious. To prevent this, countries around the world are regulating GHG emissions. Korea has decided to reduce GHG emissions by 37% compared to BAU (Business As Usual) by 2030. The transportation sector accounted for 18.58% of the domestic GHG emission, and roads accounted for 93.75% of the total. Public transportation is also included in the target of GHG reduction, and this study was conducted to reduce GHG emissions of bus public transportation, which can reduce GHG emissions while reducing the cost of road transportation. In this study, a simulation was conducted to predict the optimal GHG emission compared to the waiting time of passengers by adjusting the bus dispatch interval by implementing a greenhouse gas simulation model using Any Logic. If a more precise model is implemented in the future, it is expected that it will be used to reduce bus GHG emissions.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.184-194
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• 2023

Purpose: The purpose of this study is to identify the characteristics of C02 emissions by road before establishing a policy to reduce greenhouse gas emissions. Method: As for the analysis method, the traffic volume and speed of the road were estimated using the traffic Assignment model targeting 27 arterial road axes in Incheon Metropolitan City. And, after estimating CO2 emissions by road axis by applying this, the characteristics of each group were analyzed through cluster analysis. Result: As a result of cluster analysis using total CO2 emissions, CO2 emissions by truck vehicles, and the ratio of truck vehicle emissions to total carbon dioxide emissions, four clusters were classified. When examining the characteristics of each road included in each group, it was analyzed that the characteristics of each group appeared according to the level of impact by CO2 emissions and truck vehicles. Conclusion: It is judged that it is necessary to establish a plan in consideration of CO2 emission characteristics for road CO2 management for greenhouse gas reduction.

• Journal of Climate Change Research
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• v.3 no.4
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• pp.235-243
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• 2012

Mobile sources are one of the most significant contributors to the inventory of greenhouse gas (GHG). The administration in Korea has set a goal of cutting GHG emissions of vehicles by 34.3% compared to Business As Usual (BAU) by 2020. To achieve this goal, GHG emission standards for vehicles have been applied since 2012, and now light-duty trucks are under consideration to be included to the vehicle types that will be regulated in the new version of GHG emission standards. Therefore, this study focuses on analyzing characteristics of exhaust GHGs (CO2, CH4, and N2O) emissions of diesel light-duty trucks according to their various driving modes. GHGs emissions of diesel light-duty trucks reduced in inverse proportion to the speed of the vehicles. GHGs emissions from the combined mode were 8% and 14% lower than those from the CVS- 75 and NEDC modes, respectively.

• Journal of Korean Society of Transportation
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• v.32 no.1
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• pp.1-12
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• 2014

The tier 3 methodology used in estimation of greenhouse gas emissions from road sectors is based on mileage data. However, such data can neither accurately represent the mileage of regional unit nor have sufficient integrated data reflecting the characteristics by region, vehicle type, fuel type and road type. Such estimation of greenhouse gas emissions is not reliable. Accordingly, the purpose of this study is, firstly to accumulate activity data based on distance traveled which enables us to accurately estimate the amount of green gas emitted by regional unit(emission point), and secondly, to develop a methodology for estimation of greenhouse gas emissions using these data. To do this, the study utilizes the mileage data of Korea Transportation Safety Authority(TS), statistics of registered motor vehicles, statistical yearbook of traffic volume from the Ministry of Land, Infrastructure and Transport(MLIT), the Korea Transport Database of the Korea Transport Institute(KOTI), and average road speed by local government. Methodology for estimation by local government level(emission point) is meaningful, because it reflects traffic pattern data including flow in and out and internal traffics. Finally, to verify the methodology presented in this study, it is applied to Seoul. Both greenhouse gas estimates, one by multiplying the average mileage and the number of registered vehicles and the other by multiplying traffic volume and road extension, are less than the amount estimated by the methodology presented in this study.