[목 적] 의료기관의 핵의학 검사실에서 신규검사를 도입하거나 사용하던 시약을 변경하게 되는 경우 절차에 따라 검사의 특성이 분석되고 시약에 대한 평가가 이루어져야 한다. 그러나 요구되어지는 비교실험을 모두 수행하기 위해서는 몇 가지 필요한 조건이 충족되어야 하는데, 첫째 각 검사별로 수행하기에 충분한 검체량이 준비되어야하며, 둘째 비교실험에 적용 가능한 다양한 시약의 공급이 가능해야한다. 충분한 비교실험이 이루어졌다고 하더라도 변경된 시약에 의한 데이터 변동이 전체 환자데이터 변동을 의미하는 것에는 한계가 있으므로 검사실에서 시약이 변경되는 것에 대한 부담이 있다. 이러한 다양한 어려움으로 검사실에서의 시약변경은 제한적으로 이루어지고 있다. 본원에서는 원할한 경쟁 입찰을 도입하기 위하여 검사별로 radioimmunoassay(RIA)시약을 전수조사하고 비교실험을 통해 검사실에서 사용가능한 시약범위를 설정하였다. 이 과정을 공유하고자 하였다. [대상 및 방법] 본원 핵의학 검체 검사실에서 시행하고 있는 검사는 위탁검사를 제외하고 총 20종목이다. 각각의 검사별로 외부정도관리와 기관간 정도관리 결과보고서를 참고로 사용가능한 RIA시약을 전수 조사하였고, 각 시약에 대한 메뉴얼을 확보하였다. 각각의 시약마다 메뉴얼을 확인하여 검사 방법과 incubation시간, 검사 시 필요한 검체량, 시약량 등을 확인하여 본 검사실에서 사용가능한지 여부에 따라 시약 1차 선정을 하였다. 1차 선정된 시약을 100 test기준으로 2 kit씩 공급받아 데이터 상관성시험, 민감도, 회수율, 희석시험을 진행하였고, 비교실험 결과에 따라 시약을 2차 선정하였다. 1, 2차 선정을 통과한 시약을 경쟁 입찰리스트로 제출하였다. 검사 시약을 단수로 지정할 경우에는 1차, 2차 선정 과정에서 얻은 자료로 단수지정 사유서를 작성하였다. [결 과] 각각의 시약마다 매뉴얼을 확인하여 시약 1차 선정에서 제외되는 경우는 각 검사의 현재 Turn Around Time(TAT)보다 길어지는 경우와 검사 시 사용 시약량이 많아 장비사용이 불가능한 경우였다. 1차 선정에서 사용가능한 시약이 1개인 경우는 5종목 squamous cell carcinoma antigen(SCC Ag), 𝛽-human chorionic gonadotropin(𝛽-HCG), vitamin B12, folate, free testosterone 이었고, 2개인 경우는 8종목 (CA19-9, CA125, CA72-4, ferritin, thyroglobulin antibody(TG Ab), microsomal antibody(Mic Ab), thyroid stimulating hormone-receptor-antibody(TSH-R-Ab), calcitonin), 3개인 경우는 5종목(triiodothyronine(T3), Free T3, Free T4, TSH, intact parathyroid hormone(intact PTH)), 4개인 경우는 2종목(carcinoembryonic antigen(CEA), TG)이었다. 2차 최종 선정결과 사용가능한 시약이 3개인 것은 T3, Free T3, Free T4, TSH, CEA, 2개인 것은 TG Ab, Mic Ab, TSH-R-Ab, CA125, CA72-4, intact PTH, calcitonin이었다. 단수 지정된 종목은 ferritin, TG, CA19-9, SCC, 𝛽-HCG, vitamin B12, folate, free testosterone이었다. 2차 선정에서 제외된 사유에는 비교실험을 위한 시약공급이 안된 경우와 데이터 재현성에 문제가 있었던 경우, 데이터 변동에 대한 수용이 불가능하다고 판단되는 경우였다. 비교실험 시 가장 문제가 되는 부분은 검체 수집이었다. 검사건수가 많고 검사 시 필요한 검체량이 적은 경우에는 문제가 되지 않았지만, 검사건수가 적은 경우(월 100건 이하)에는 다양한 농도 검체를 수집하기가 어려웠으며, 한번 검사 시 필요한 검체량이 상대적으로 많은 경우(100 uL이상)에는 회수율시험을 진행하기가 어려웠다. 또한 민감도 측정이나 희석시험을 위한 희석액이나 표준액0 물질이 부족한 경우도 문제점 중의 하나였다. [결 론] 검사시약 변경을 위한 비교실험 시 다양하고 충분한 검체 수집을 위해 적정한 준비기간이 필요하다. 또한 1회 검사 시 필요한 검체량 및 시약량에 따라 비교실험 시 필요한 총 검체량, 시약량 범위를 설정해 놓는다면 비교실험을 진행할 때마다 검체 수집과 실험계획을 세우는 데 부담이 줄어들 것이다.
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.