• 제목/요약/키워드: Management assessment

검색결과 6,272건 처리시간 0.027초

주성분(主成分) 및 정준상관분석(正準相關分析)에 의(依)한 수간성장(樹幹成長) 해석(解析)에 관(關)하여 (An Analytical Study on the Stem-Growth by the Principal Component and Canonical Correlation Analyses)

  • 이광남
    • 한국산림과학회지
    • /
    • 제70권1호
    • /
    • pp.7-16
    • /
    • 1985
  • 임목(林木)의 주체성인(主體成因)인 수간(樹幹)에 대한 각종(各種) 성장인자간(成長因子間)의 정준상관(正準相關)과 그의 관계적(關係的) 배경(背景) 및 수간(樹幹)의 총합적(總合的)인 변동분석(變動分析)에 의(依)한 수간적(樹幹的) 특징(特徵)을 파악(把握)함에 있어, 그의 최적기법(最適技法)을 탐색(探索)하기 위한 시도(試圖)로서 일본(日本)잎갈나무(Larix leptolepis)에 주성분(主成分) 및 정준상관분석법(正準相關分析法)을 도입적용(導入適用)하고, 얻어진 결과(結果)를 다음과 같이 요약(要約)한다. 1) 정형수(正形數)($x_8$)를 제외(除外)한 모든 성장인자(成長因子) 즉(卽), 수고(樹高)($x_1$), 지하고(枝下高)($x_2$), 망고(望高)($x_3$), 흉고직경(胸高直徑)($x_4$), 중앙직경(中央直徑)($x_5$), 수관폭(樹冠幅)($x_6$) 및 간재적(幹材積)($x_7$) 등(等)의 각(各) 인자간(因子間)에 강약간(強弱間)의 상관(相關)이 있으며, 특(特)히 흉고직경(胸高直徑), 수고(樹高) 및 중앙직경(中央直徑) 등(等)은 간재적(幹材積)과 고도(高度)의 상관(相關)이 있다(표(表) l 참조(參照)). 2) (1) 상장성장인자(上長成長因子)인 수고(樹高), 지하고(枝下高) 및 망고(望高) 등(等)의 합성변량(合成變量)과 간재적간(幹材積間), (2) 비대성장인자(肥大成長因子)인 흉고직경(胸高直徑), 중앙직경(中央直徑) 및 수관폭(樹冠幅) 등(等)의 합성변량(合成變量)과 간재적간(幹材積間), (3) 상장(上長) 및 비대성장인자(肥大成長因子)를 총망라(總網羅)한 6개인자(個因子)의 합성변량(合成變量)과 간재적간(幹材積間)의 정준상관계수(正準相關係數)와 정준변량(正準變量)이 각각(各各) $${(1)\;{\gamma}_{u1,v1}=0.82980^{**},\;\{u_1=1.00000x_7\\v_1=1.08323x_1-0.04299x_2-0.07080x_3}\\{(2)\;{\gamma}_{u1,v1}=0.98198^{**},\;\{u_1=1.00000x_7\\v_1=0.86433x_4+0.11996x_5+0.02917x_6}\\{(3)\;{\gamma}_{u1,v1}=0.98700^{**},\;\{u_1=1.00000x_7\\v1=0.12948x_1+0.00291x_2+0.03076x_3+0.76707x_4+0.09107x_5+0.02576x_6}$$ 등(等)과 같이 되어, 어느 경우(境遇)에서도 고도(高度)의 정준상관(正準相關)을 가지며, (1)의 경우(境遇)에는 수고(樹高)가, (2)의 경우(境遇)에는 흉고직경(胸高直徑)이, (3)의 경우(境遇)에는 흉고직경(胸高直徑)과 수고(樹高)가 각각(各各)의 정준상관(正準相關)에 절대적인 기여(寄與)를 하는 것으로서, 각종(各種) 질적성장(質的成長)의 총합특성(總合特性)은 이들 인자(因子)의 막강한 영향력(影響力)에 의해서 형성(形成)되며, 특(特)히 (3)의 경우에서 간재적(幹材積)과의 정준상관(正準相關)에 미치는 흉고직경(胸高直徑)의 영향력(影響力)은 기타(其他)의 인자(因子)에 비(比)하여 판이(判異)하게 큰 것으로 밝혀지고 있다(표(表) 2 참조(參照)). 3) 상장성장인자(上長成長因子)인 수고(樹高), 지하고(枝下高) 및 망고(望高) 등(等)의 합성변량(合成變量)과 비대성장인자(肥大成長因子)인 흉고직경(胸高直徑), 중앙직경(中央直徑) 및 수관폭(樹冠幅) 등(等)의 합성변량간(合成變量間)의 정준상관계수(正準相關係數)와 정준변량(正準變量)이 $${\gamma}_{u1,v1}=0.78556^{**},\;\{u_1=1.20569x_1-0.04444x_2-0.21696x_3\\v_1=1.09571x_4-0.14076z_5+0.05285z_6$$와 같이 됨에 따라, 각종 상장성장인자(上長成長因子)와 비대성장인자간(肥大成長因子間)의 고도(高度)의 정준상관(正準相關)에 있어 수고(樹高)와 흉고직경(胸高直徑)만의 기여도(寄與度)가 극(極)히 현저한 것으로서, 상장성장(上長成長)의 총합특성(總合特性)은 수고(樹高)에 의해서, 비대성장(肥大成長)의 총합특성(總合特性)은 흉고직경(胸高直徑)에 의해서 각각(各各) 형성(形成)된다는 사실(事實)이 확인(確認)된 것이다. 따라서 양인자(兩因子)에 대한 간재적계측(幹材積計測)에 있어서의 필수유력인(必須有力因子)로서의 과학성(科學性)이 입증(立證)된 것이라 생각한다(표(表) 2 참조(參照)). 4) 수간(樹幹)의 8개성장인자(個成長因子) 즉(卽), 8차원(次元)의 정보(情報)(특성치(特性値))를 설정(設定)된 유효목표(有效目標) 85%에 따라 3차원(次元)으로 간략화(簡約化)된 총합특성치(總合特性値) 즉(卽), 제(第) 1 ~ 제(第) 3 주성분(主成分)은 다음과 같다. 제(第) 1 주성분(主成分)($Z_1$); $Z_1=0.40192x_1+0.23693x_2+0.37047x_3+0.41745x_4+0.41629x_5+0.33454x_60.42798x_7+0.04923x_8$ 제(第) 2 주성분(主成分)($Z_2$) ; $z_2=-0.09306x_1-0.34707x_2+0.08372x_3-0.03239x_4+0.11152x_5+0.00012x_6+0.02407x_7+0.92185x_8$ 제(第) 3 주성분(主成分)($Z_3$) ; $Z_3=0.19832x_1+0.68210x_2+0.35824x_3-0.22522x_4-0.20876x_5-0.42373x_6-0.15055x_7+0.26562x_8$ 제(第) 1 주성분(主成分)($Z_1$)은 기여율(寄與率)이 63.26%나 되는 매우 높은 정보흡수력(情報吸收力)을 가진 "크기의 인자(因子)(size factor)"로서, 그의 주성분득점(主成分得點)(principal component score)은 인자부하량(因子負荷量)이 매우 높은 간재적(幹材積), 흉고직경(胸高直徑), 중앙직경(中央直徑) 및 수고(樹高) 등(等)에 의해써 결정(決定)되며, 제(第) 2 주성분(主成分)($Z_2$)은 입체적(立體的) 형상(形狀)의 지표(指標) 즉(卽), 수간(樹幹)의 입체적(立體的) 상사성(相似性)과 완구도(完溝度)를 나타내주는 "형상(形狀)의 인자(因子)(shape factor)"로서, 그의 score는 정형수(正形數)의 절대적(絶對的)인 영향력(影響力)에 의(依)해서 형성(形成)되며, 제(第) 3 주성분(主成分)($Z_3$)은 상장성장(上長成長)과 비대성장(肥大成長)과의 역관계(逆關係)의 현상(現象) 즉(卽), 수간(樹幹)의 세장(細長)(또는 굵고 짧음)의 정도를 표시(表示)하는 성장형상(成長形狀)의 지표(指標)로서, 이는 제(第) 2의 "형상(形狀)의 인자(因子)"가 된다. 이상(以上) 3개주성분(個主成分)은 그의 누적기여율(累積寄與率)이 88.36%로서 만족스러운 정보흡수역량(情報吸收力量)을 지니고 있다(표(表) 3 참조(參照)). 5) 본(本) 연구(硏究)에 적용(適用)된 주성분(主成分) 및 정준(正準) 상관분석법(相關分析法)은 적극적(積極的)인 이용개발(利用開發)에 따라서는 삼림계측(森林計測)(임목성장(林木成長)), 지위판정분류(地位判定分類), 삼림(森林) 및 임산업(林産業)의 경영진단(經營診斷), 임산가공(林産加工)(품(品))의 생산관리(生産管理) 및 기지(其地) 총합특성치(總合特性値)의 산정(算定)을 필요(必要)로 하는 분야(分野)에 많은 기여(寄與)가 있을 것으로 사료(思料)된다.

  • PDF

한정된 O-D조사자료를 이용한 주 전체의 트럭교통예측방법 개발 (DEVELOPMENT OF STATEWIDE TRUCK TRAFFIC FORECASTING METHOD BY USING LIMITED O-D SURVEY DATA)

  • 박만배
    • 대한교통학회:학술대회논문집
    • /
    • 대한교통학회 1995년도 제27회 학술발표회
    • /
    • pp.101-113
    • /
    • 1995
  • The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

  • PDF