Bivariate regional frequency analysis of extreme rainfalls in Korea

Shin Ju-Young,Jeong Changsam,Ahn Hyunjun,Heo Jun-Haeng 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.9

다변량 빈도해석과 지역빈도해석의 장점을 동시에 가지는 다변량 지역빈도해석은 다양한 변수를 고려함으로써 수문 현상에 대하여 많은 정보를 얻을 수 있고 많은 가용 자료 수로 인하여 높은 정확도의 분석결과를 도출할 수 있다. 현재까지는 우리나라의 강우 자료를 이용하여 다변량 지역빈도해석이 시도된 적이 없어 국내의 강우 자료를 대상으로 다변량 지역빈도해석의 적용성을 검토할 필요가 있다. 본 연구에서는 다변량 지역빈도해석의 매개변수 추정, 최적 분포형 선정, 확률수문량 성장곡선 추정 등에 집중하여 이변량 수문자료인 연 최대 강우량-지속기간 자료에 대하여 이변 량 지역빈도해석의 적용성을 평가하였다. 기상청 71개 지점에 대하여 분석을 실시하였다. 본 연구를 통해 적용된 지역강우자료의 최적 copula 모형으로는 Frank와 Gumbel copula 모형이 선택되었고 주변분포형에 대해서는 지역별로 Gumbel과 대수정규분포와 같은 다양한 분포형이 최적 분포형으로 선택되었다. 상대제곱근오차(relative root mean square error)를 기준으로 지역빈도해석이 지점빈도해석보다 안정적이고 정확한 확률수문량 곡선 추정을 하였다. 이변량 강우분석에서 지역빈도해석을 적용하면 안정적인 수공구조물 설계기준 제시와 강우-지속기간 관계를 모형화 할 수 있을 것으로 기대된다. Multivariate regional frequency analysis has advantages of regional and multivariate framework as adopting a large number of regional dataset and modeling phenomena that cannot be considered in the univariate frequency analysis. To the best of our knowledge, the multivariate regional frequency analysis has not been employed for hydrological variables in South Korea. Applicability of the multivariate regional frequency analysis should be investigated for the hydrological variable in South Korea in order to improve our capacity to model the hydrological variables. The current study focused on estimating parameters of regional copula and regional marginal models, selecting the most appropriate distribution models, and estimating regional multivariate growth curve in the multivariate regional frequency analysis. Annual maximum rainfall and duration data observed at 71 stations were used for the analysis. The results of the current study indicate that Frank and Gumbel copula models were selected as the most appropriate regional copula models for the employed regions. Several distributions, e.g. Gumbel and log-normal, were the representative regional marginal models. Based on relative root mean square error of the quantile growth curves, the multivariate regional frequency analysis provided more stable and accurate quantiles than the multivariate at-site frequency analysis, especially for long return periods. Application of regional frequency analysis in bivariate rainfall-duration analysis can provide more stable quantile estimation for hydraulic infrastructure design criteria and accurate modelling of rainfall-duration relationship.

임상의를 위한 다변량 분석의 실제

오주한(Joo Han Oh),정석원(Seok Won Chung) 대한견주관절의학회 2013 대한견주관절의학회지 Vol.16 No.1

임상 의학의 연구에 사용되는 대표적 다변량 분석 방법은 다중 회귀 분석 방법인데, 이는 인과 관계를 토대로 여러 개의 변수에 의한 한꺼번에의 영향력을 분석하기 위한 방법이다. 다중 회귀 분석은 기본적으로 회귀분석의 기본 가정을 만족해야 함은 물론, 여러 개의 독립 변수들이 포함되기 때문에 변수들을 모형에 포함시키는 방법 및 다중 공선성 문제에 대한 고려가 필요하다. 다중 회귀 분석 모형의 설명력은 결정 계수 R2으로 표현되어 1에 가까울수록 설명력이 크며, 각 독립 변수들의 결과에의 영향력은 회귀 계수인 β값으로 표현된다. 다중 회귀 분석은 종속 변수의 형태에 따라 다중 선형 회귀 분석, 다중 로지스틱 회귀 분석, 콕스 회귀 분석으로 나눌 수 있다. 종속 변수가 연속 변수인 경우 다중 선형 회귀 분석, 범주형 변수인 경우 다중 로지스틱 회귀 분석, 시간의 영향을 고려한 상태 변수인 경우는 콕스 회귀 분석을 시행해야 하며, 각각 결과에의 영향력은 회귀 계수 β, 교차비, 위험비로 평가한다. 이러한 다변량 분석에 대한 이해는 연구를 계획하고 결과를 분석하고자 하는 임상 의사에게 있어 보다 효율적인 연구를 위해 필수적인 소양이라고 할 수 있다. In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, R2 and the influence of independent variables on result as a regression coefficient, β. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient β, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

Multivariate survival analysis of the patients with recurrent endometrial cancer

Tetsuji Odagiri,Hidemichi Watari,Masayoshi Hosaka,Takashi Mitamura,Yousuke Konno,Tatsuya Kato,Noriko Kobayashi,Satoko Sudo,Mahito Takeda,Masanori Kaneuchi,Noriaki Sakuragi 대한부인종양학회 2011 Journal of Gynecologic Oncology Vol.22 No.1

Objective: Few studies on the prognosticators of the patients with recurrent endometrial cancer after relapse have been reported in the literature. The aim of this study was to determine the prognosticators after relapse in patients with recurrent endometrial cancer who underwent primary complete cytoreductive surgery and adjuvant chemotherapy. Methods: Thirty-five patients with recurrent endometrial cancer were included in this retrospective analysis. The prognostic significance of several clinicopathological factors including histologic type, risk for recurrence, time to relapse after primary surgery, number of relapse sites, site of relapse, treatment modality, and complete resection of recurrent tumors were evaluated. Survival analyses were performed by Kaplan-Meier curves and the log-rank test. Independent prognostic factors were determined by multivariate Cox regression analysis. Results: Among the clinicopathological factors analyzed, histologic type (p=0.04), time to relapse after primary surgery (p=0.03), and the number of relapse sites (p=0.03) were significantly related to survival after relapse. Multivariate analysis revealed that time to relapse after primary surgery (hazard ratio, 6.8; p=0.004) and the number of relapse sites (hazard ratio, 11.1; p=0.002) were independent prognostic factors for survival after relapse. Survival after relapse could be stratified into three groups by the combination of two independent prognostic factors. Conclusion: We conclude that time to relapse after primary surgery, and the number of relapse sites were independent prognostic factors for survival after relapse in patients with recurrent endometrial cancer. Objective: Few studies on the prognosticators of the patients with recurrent endometrial cancer after relapse have been reported in the literature. The aim of this study was to determine the prognosticators after relapse in patients with recurrent endometrial cancer who underwent primary complete cytoreductive surgery and adjuvant chemotherapy. Methods: Thirty-five patients with recurrent endometrial cancer were included in this retrospective analysis. The prognostic significance of several clinicopathological factors including histologic type, risk for recurrence, time to relapse after primary surgery, number of relapse sites, site of relapse, treatment modality, and complete resection of recurrent tumors were evaluated. Survival analyses were performed by Kaplan-Meier curves and the log-rank test. Independent prognostic factors were determined by multivariate Cox regression analysis. Results: Among the clinicopathological factors analyzed, histologic type (p=0.04), time to relapse after primary surgery (p=0.03), and the number of relapse sites (p=0.03) were significantly related to survival after relapse. Multivariate analysis revealed that time to relapse after primary surgery (hazard ratio, 6.8; p=0.004) and the number of relapse sites (hazard ratio, 11.1; p=0.002) were independent prognostic factors for survival after relapse. Survival after relapse could be stratified into three groups by the combination of two independent prognostic factors. Conclusion: We conclude that time to relapse after primary surgery, and the number of relapse sites were independent prognostic factors for survival after relapse in patients with recurrent endometrial cancer.

다변량 통계분석기법을 활용한 금강수계 14개 호소의 수질평가

김진호,주진철,안채민,황대호 대한환경공학회 2021 대한환경공학회지 Vol.43 No.3

Objectives:14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality. Methods:Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering. Results and Discussion:From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants. Conclusions:Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis. 목적:금강수계 내 14개 호소의 수질 특성별 군집화를 위해 요인분석의 결과(factor 1 기반)를 활용해 호소를 군집 및 분류하고 오염원(토지계, 생활계, 축산계)과 수질인자 간 상관분석(correlation analysis)을 통해 오염원이 수질에 미치는 영향을 조사하였다. 방법:금강수계 내 14개 호소의 최근 5년(2014~2018)의 다양한 수질항목 자료를 활용해 군집분석(cluster analysis, CA), 주성분분석(principle component analysis, PCA), 요인분석(factor analysis, FA)을 활용해 수질에 영향을 미치는 주성분을 도출하고, 요인분석을 통해 나온 결과를 바탕으로 실제 오염원과의 상관성을 분석하였다. 결과 및 토의:14개 호소의 요인분석 결과 3~6개의 요인이 추출되었으며 평균 74%의 설명력을 나타냈다. 요인 1에 추출된 수질인자를 바탕으로 호소를 분류한 결과, 질소 요인과 계절 요인으로 분류된 호소는 간월호, 금강호, 삽교호이며, 유기오염과 내부생산으로 분류된 호소는 탑정지, 대호, 석문호, 용담호이며, 유기오염과 내부생산 그리고 인 요인으로 분류된 호소는 부남호, 예당지, 청천지이다. 나머지 보령호, 대청호, 초평지, 송악지는 기타 호소로 분류되었다. 요인분석을 통해 나온 결과와 실제 오염원과의 상관성을 분석한 결과, 토지계, 생활계, 축산계 오염원과 높은 상관성을 나타낸 호소는 간월호, 탑정지, 대호, 부남호, 대청호이며 이들 호소는 특정(토지계, 생활계, 축산계) 오염원의 지속적인 관리가 필요할 것으로 판단된다. 수질과 오염원 관리 측면에서 나쁨으로 평가된 간월호, 삽교호, 석문호는 수질개선을 위한 대책과 오염원 유입 방지 대책이 필요할 것으로 판단된다. 결론:호소의 수질은 유입수, 형태학적 요소, 수문학적 요소, 내부생산요소, 오염원 등의 복합적인 작용으로 인한 결과로서 매우 복잡한 인과관계를 형성하고 있으나 다변량 통계분석 등의 통계학적인 기법을 통해 호소 특성에 맞는 맞춤형 유역 및 수질관리 방안의 도출이 가능하다.

역정규 손실함수를 이용한 다변량 공정능력지수

문혜진,정영배 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.2

In the industrial fields, the process capability index has been using to evaluate the variation of quality in the process. The traditional process capability indices such as Cp, Cpk, Cpm and C+pm have been applied in the industrial fields. These traditional process capability indices are mainly applied in the univariate analysis. However, the main streams in the recent industry are the multivariate manufacturing process and the multiple quality characteristics are corrected each other. Therefore, the multivariate statistical method should be used in the process capability analysis. The multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. Hence, the purpose of the study is to develop a more effective multivariate process index (MCpI) using the multivariate inverted normal loss function. The multivariate inverted normal loss function has the flexibility for the any type of the symmetrical and asymmetrical loss functions as well as the economic information. Especially, the proposed modeling method for the multivariate inverted normal loss function (MINLF) and the expected loss from MINLF in this paper can be applied to the any type of the symmetrical and asymmetrical loss functions. And this modeling method can be easily expanded from a bivariate case to a multivariate case.

염색폐수 유기물 지표의 다변량 통계분석

조영범,안준수,김채호,신동철 대한환경공학회 2024 대한환경공학회지 Vol.46 No.2

Objectives:Since 2016, TOC (Total Organic Carbon) has replaced COD (Chemical Oxygen Demand) as an organic indicator for effluent wastewater quality standards. However, the distribution of organic substances by process in wastewater treatment facilities is not properly identified, making it difficult to secure stable treated wastewater quality. Therefore, in this study, we identified the correlation between TOC and existing organic matter indicators in raw wastewater, primary treated, secondary treated, and effluent wastewater for dyeing wastewater.Methods:Samples for each process were collected twice a week, a total of 24 times, from a dyeing wastewater treatment plant located in Y-city, Gyeonggi-do, and organic pollutant indicators (TOC, COD_Cr, COD_Mn, BOD₅) were analyzed. TOC was analyzed by the NPOC (non-purgeable organic carbon) method using TOC-VCHP (Shimadzu, Japan). Using the analysis results, the characteristics of organic pollutants in dyeing wastewater were analyzed. In addition, multivariate statistical analysis was performed using SPSS to analyze correlations between organic pollutant indicators and principal component analysis.Results and Discussion:As a result of multivariate statistical analysis, TOC was inflowed at an average of 574.9 mg/L and treated at 58.2mg/L. In the case of COD_Cr, COD_Mn, and BOD₅, the inflow was 1,644, 448.9, and 440.7 mg/L and was treated at 98.2, 39.7, and 10.8mg/L. When evaluated based on effluent water quality standards, all of them satisfied the Region III standards, but were discharged at a relatively high level compared to the TOC concentration of sewage treatment plants effluent. As a result of comparing correlations between organic matter indicators through Pearson correlation analysis, the inflow raw water shows a high positive correlation with TOC:TCOD_Cr (r=0.720), TOC:TCOD_Mn (r=0.636), and TOC:TBOD₅ (r=0.302) showed low correlation. This is reason to be due to the fact that most organic substances in dyeing wastewater are non-degradable substances and have low biodegradability. As a result of principal component analysis of influent, primary treated, and final treated, three main components each (two for final treated) were extracted, with cumulative contribution rates of 80.1%, 83.2%, and 95.6%.Conclusion:Because the properties of wastewater differ greatly depending on the type of leather and chemicals handled at the dyeing factory, the correlation between influent water was low, but the correlation between treated water and treated water was relatively high. The correlation between processes in wastewater treatment facilities also tended to increase toward later processes. It is believed that the above statistical analysis can be used as basic data for effective organic matter management. 목적 : 2016년부터 방류수 수질기준의 유기물 지표로써 TOC(Total Organic Carbon)가 COD(Chemical Oxygen Demand)를 대체하여 관리하고 있다. 그러나 폐수처리시설의 공정별 유기물질 분포의 파악이 제대로 이루어지지않아 안정적인 처리수질 확보에 어려움을 느끼고 있다. 따라서 본 연구에서는 염색폐수를 대상으로 원수와 1차처리수, 2차처리수, 방류수의 TOC와 기존 유기물질 지표와의 상관관계를 파악하였다. 방법 : 경기도 Y시에 위치한 염색폐수처리시설을 대상으로 각 공정별 시료를 주 2회, 총 24회를 채취하여 유기오염물 지표(TOC, CODCr, CODMn, BOD5)를 분석하였다. TOC는 TOC-VCHP (Shimadzu, Japan)을 이용하여 NPOC (non-purgeable organic carbon)법으로 분석하였다. 분석 결과를 이용하여 염색폐수의 유기오염물질 특성을 분석하였다. 또한 SPSS를 이용하여 다변량 통계분석을 실시하여 유기오염물 지표간의 상관관계, 주성분 분석을 하였다. 결과 및 토의 : 다변량 통계분석 분석결과, TOC는 평균 574.9 mg/L로 유입되어 58.2 mg/L로 처리되었다. CODCr, CODMn, BOD5의 경우 1,644, 448.9, 440.7 mg/L로 유입되어 98.2, 39.7, 10.8 mg/L로 처리되었다. 방류수 수질 기준으로 평가하였을 때, 모두 III지역 기준을 만족시키고 있었으나 하수처리시설 방류수의 TOC 농도에 비해 상대적으로 높은 수준으로 방류되고 있다. Pearson correlation analysis를 통하여 유기물 지표간 단순상관성을 비교한 결과, 유입 원수는 TOC:TCODCr (r=0.720), TOC:TCODMn (r=0.636)으로 높은 양의 상관관계를 보이고 있으며 TOC: TBOD5 (r=0.302)은 낮은 상관성을 보였으며 염색폐수내 유기물질이 대부분 난분해성 물질로 생분해성이 낮음에서기인한 것으로 판단된다. 유입수와 1차 처리수, 최종처리수의 주성분 분석 결과, 각 3개(최종처리수는 2개)의 주성분이 추출되었으며, 누적 기여율은 80.1%, 83.2%, 95.6%를 차지했다. 결론 : 염색 공장에서 취급하는 가죽의 종류와 약품 등에 따라 폐수 성상 차이가 심하기 때문에 유입수의 상관성은낮게 나타났으나 처리수는 비교적 높은 상관관계를 보였다. 폐수처리시설의 공정별 상관성도 후단 공정으로 갈수록 높아지는 경향을 보였다. 위의 통계 분석은 효과적인 유기물질 관리를 위한 기초자료로써 활용이 가능할 것으로 판단된다.

Multivariate Process Capability Index Using Inverted Normal Loss Function

Hye-Jin Moon(문혜진),Young-Bae Chung(정영배) 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.2

In the industrial fields, the process capability index has been using to evaluate the variation of quality in the process. The traditional process capability indices such as Cp, Cpk,Cpm and have been applied in the industrial fields. These traditional process capability indices are mainly applied in the univariate analysis. However, the main streams in the recent industry are the multivariate manufacturing process and the multiple quality characteristics are corrected each other. Therefore, the multivariate statistical method should be used in the process capability analysis. The multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. Hence, the purpose of the study is to develop a more effective multivariate process index (????MC ) using the multivariate inverted normal loss function. The multivariate inverted normal loss function has the flexibility for the any type of the symmetrical and asymmetrical loss functions as well as the economic information. Especially, the proposed modeling method for the multivariate inverted normal loss function (MINLF) and the expected loss from MINLF in this paper can be applied to the any type of the symmetrical and asymmetrical loss functions. And this modeling method can be easily expanded from a bivariate case to a multivariate case.

Dynamic pattern decoding of source-reconstructed MEG or EEG data: Perspective of multivariate pattern analysis and signal leakage

Gohel, Bakul,Lim, Sanghyun,Kim, Min-Young,Kwon, Hyukchan,Kim, Kiwoong Elsevier 2018 Computers in biology and medicine Vol.93 No.-

<P><B>Abstract</B></P> <P>Recently, an increasing number of studies have employed multivariate pattern analysis (MVPA) rather than univariate analysis for the dynamic pattern decoding of event-related responses recorded with a MEG/EEG sensor. The use of the MVPA approach for source-reconstructed MEG/EEG data is uncommon. For these data, we need to consider the source orientation information and the signal leakage among brain regions. In the present study, we evaluate the perspective of the MVPA approach in the context of source orientation information and signal leakage in source-reconstructed MEG data. We perform face vs. tool object category decoding (FvsT-OCD) of event-related responses from single or multiple voxels from a brain region using a univariate analysis approach and/or the MVPA approach. We also propose and perform symmetric signal leakage correction of source-reconstructed data using an independent component analysis-based approach. FvsT-OCD using single voxel information shows higher sensitivity for the MVPA approach than univariate analysis, as the MVPA approach efficiently utilizes information on all three dipole orientations and is less affected by inter-subject variability. The MVPA approach shows higher sensitivity for FvsT-OCD when considering information from multiple voxels than for a single voxel in a brain region. This finding suggests that the MVPA approach captures the latent multivoxel distributed pattern. However, the results may be partly or entirely attributable to signal leakage between brain regions, as the sensitivity is substantially reduced after signal leakage correction. A consideration of signal leakage is therefore essential during the evaluation of MVPA outcomes.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The multivariate pattern analysis (MVPA) is increasingly used in cognitive studies. </LI> <LI> Use of the MVPA approach is uncommon for source-reconstructed M/EEG data analysis. </LI> <LI> The study takes account of the source orientation and signal leakage issue. </LI> <LI> The MVPA method can effectively use information from all three source orientations. </LI> <LI> Signal leakage and its correction significantly influence the MVPA outcomes. </LI> </UL> </P>

Multivariate statistical analysis of the comparative antioxidant activity of the total phenolics and tannins in the water and ethanol extracts of dried goji berry (Lycium chinense) fruits

Joo-Shin Kim,Haklin Alex Kimm 한국식품과학회 2019 한국식품과학회지 Vol.51 No.3

Antioxidant activity in water and ethanol extracts of dried Lycium chinense fruit, as a result of the total phenolic and tannin content, was measured using a number of chemical and biochemical assays for radical scavenging and inhibition of lipid peroxidation, with the analysis being extended by applying a bootstrapping statistical method. Previous statistical analyses mostly provided linear correlation and regression analyses between antioxidant activity and increasing concentrations of phenolics and tannins in a concentration-dependent mode. The present study showed that multiple component or multivariate analysis by applying multiple regression analysis or regression planes proved more informative than linear regression analysis of the relationship between the concentration of individual components and antioxidant activity. In this paper, we represented the multivariate analysis of antioxidant activities of both phenolic and tannin contents combined in the water and ethanol extracts, which revealed the hidden observations that were not evident from linear statistical analysis.

EXPERIMENTAL ANALYSIS OF DRIVING PATTERNS AND FUEL ECONOMY FOR PASSENGER CARS IN SEOUL

Sa, J.-S.,Chung, N.-H.,Sunwoo, M.-H. The Korean Society of Automotive Engineers 2003 International journal of automotive technology Vol.4 No.2

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.