메스암페타민(methamphetamine, MA)과 암페타민(amphetamine, AP)은 전세계적으로 널리 퍼진합성 흥분제로 보건 및 사회 문제, 경제적 비용 발생 등의 다양한 문제를 일으킨다. 또한 이러한 마약은 중독 및 남용 가능성이 매우 높기 때문에 확산 방지를 위한 방안의 하나로 복용자의 생체시료에서 이들 마약을 검출하기 위한 분석법 개발이 필요하다. 따라서 본 연구에서는 액체크로마토그래피-질량분석법(liquid chromatography-tandem mass spectrometry, LC-MS/MS)를 이용하여 소변에서 MA와 4-하이드록시메스암페타민(4-hydroxymethamphetamine, 4HMA), AP, 4-하이드록시암페타민(4-hydroxyamphetamine, 4HA)을 동시에 분석할 수 있는 분석법을 개발하였고 유효성 평가를 수행하였다. 전처리 시간 단축을 위해 50000 g에서 3 min 동안 초고속원심분리 후 시료 희석 주입법을 이용하여 LC-MS/MS에 주입하였다. 분리관은 역상 C18 컬럼을 사용하였고, 정량분석을 위해 MRM (multiple reaction monitoring) 모드를 적용하였다. 가중계수
상품자산(Commodity Asset)은 주식, 채권과 같은 전통자산의 포트폴리오의 안정성을 높이기 위한 대체투자자산으로 자산배분의 형태로 투자되고 있지만 주식이나 채권 자산에 비해 자산배분에 대한 모델이나 투자전략에 대한 연구가 부족한 실정이다. 최근 발전한 기계학습(Machine Learning) 연구는 증권시장의 투자부분에서 적극적으로 활용되고 있는데, 기존 투자모델의 한계점을 개선하는 좋은 성과를 나타내고 있다. 본 연구는 이러한 기계학습의 한 기법인 SVM(Support Vector Machine)을 이용하여 상품자산에 투자하는 모델을 제안하고자 한다. 기계학습을 활용한 상품자산에 관한 기존 연구는 주로 상품가격의 예측을 목적으로 수행되었고 상품을 투자자산으로 자산배분에 관한 연구는 찾기 힘들었다. SVM을 통한 예측대상은 투자 가능한 대표적인 4개의 상품지수(Commodity Index)인 골드만삭스 상품지수, 다우존스 UBS 상품지수, 톰슨로이터 CRB상품지수, 로저스 인터내셔날 상품지수와 대표적인 상품선물(Commodity Futures)로 구성된 포트폴리오 그리고 개별 상품선물이다. 개별상품은 에너지, 농산물, 금속 상품에서 대표적인 상품인 원유와 천연가스, 옥수수와 밀, 금과 은을 이용하였다. 상품자산은 전반적인 경제활동 영역에 영향을 받기 때문에 거시경제지표를 통하여 투자모델을 설정하였다. 주가지수, 무역지표, 고용지표, 경기선행지표 등 19가지의 경제지표를 이용하여 상품지수와 상품선물의 등락을 예측하여 투자성과를 예측하는 연구를 수행한 결과, 투자모델을 활용하여 상품선물을 리밸런싱(Rebalancing)하는 포트폴리오가 가장 우수한 성과를 나타냈다. 또한, 기존의 대표적인 상품지수에 투자하는 것 보다 상품선물로 구성된 포트폴리오에 투자하는 것이 우수한 성과를 얻었으며 상품선물 중에서도 에너지 섹터의 선물을 제외한 포트폴리오의 성과가 더 향상된 성과를 나타남을 증명하였다. 본 연구에서는 포트폴리오 성과 향상을 위해 기존에 널리 알려진 전통적 주식, 채권, 현금 포트폴리오에 상품자산을 배분하고자 할 때 투자대상은 상품지수에 투자하는 것이 아닌 개별 상품선물을 선정하여 자체적 상품선물 포트폴리오를 구성하고 그 방법으로는 기간마다 강세가 예측되는 개별 선물만을 골라서 포트폴리오를 재구성하는 것이 효과적인 투자모델이라는 것을 제안한다.
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.