• 한국식품영양과학회지
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• 제36권9호
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• pp.1172-1181
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• 2007

본 연구는 전북지역 학교 영양사 226명을 대상으로 건강기능식품 섭취실태 및 인식도를 알아보고자 설문조사를 실시하였으며 연구결과를 요약하면 다음과 같다. 조사대상자의 일반적 특징은 여자가 98.7%였고, 연령은 30${\sim}$39세가 73.5%로 가장 많았다. 학력은 대졸이 82.7%로 가장 많았으며, 결혼은 기혼이 78.8%를 차지하였다. 현재 자신의 건강상태 인지는 ‘보통이다’가 53.5%, ‘건강한 편이다’ 34.1% 순으로 나타났다. 건강기능식품에 관한 법률 제정 및 시행 사실을 69.0%가 모르고 있다고 응답하였고, 식품과 질병과의 관계 인지도는 ‘매우 관계가 있다’가 68.6%, ‘어느 정도 관계가 있다’가 31.4%로 조사되어 식품과 질병이 밀접한 관계가 있다고 인지하고 있었다. 건강기능식품 제조${\cdot}$판매 회사의 홍보나 광고에 대해 93.8%가 '허위 과대 선전이 많은 것 같다’고 응답하였고, 유통구조에 대해서도 60%가 '잘 되어있지 않다’고 응답하여 건강기능식품 제조회사에 대한 신뢰도가 낮게 나타났다. 건강기능식품의 효율적 관리를 위한 국가에서 관심을 가져야 할 분야는 안전성 제고 및 효능 검증이 79.6%로 가장 많이 나타났다. 건강에 영향을 주는 요인은 식습관(3.9)>스트레스 해소(3.73)>규칙적인 생활(3.7)>휴식 및 수면(3.66)>운동(3.62) 순으로 조사되었다. 반면 건강기능식품(2.07)은 가장 낮은 점수를 보여 건강에 미치는 영향이 적다고 인지하고 있었다. 건강기능식품의 섭취실태는 61.9%가 섭취한 경험이 있었고, 섭취종류는 영양보충용제품(57.9%)>홍삼제품(52.9%)>클로렐라제품(30.0%) 순으로 섭취하였다. 건강기능식품 섭취이유는 피로회복(25.7%)>질병의 예방(22.9%)>영양보충(22.1)>주변의 권유(11.4%) 순이었다. 구입방법은 방문판매원을 통해서가 40%로 가장 높게 나타났고, 평균구입비용은 26만원 이상이 25.7%로 가장 높게 나타났다. 제품 표시 설명서 이해정도는 42.1%가 이해하지 못하는 것으로 조사되었고, 섭취 후 효과는 ‘그저 그러함’이 65.7%로 가장 높게 조사되었고 22.1%만이 재구매 의사가 있었다. 건강기능식품을 섭취하지 않는 이유는 ‘효능을 믿을 수가 없어서’가 68.6%로 가장 높게 조사되었으며 건강기능식품의 부정적인 견해는 ‘비싸게 판매’ 34.3%, ‘과대선전으로 소비자를 속인다’와 ‘안정성에 대한 보장이나 정보가 부족하다’가 각각 27.9%로 나타나 건강기능식품에 대한 부정적인 생각을 가지고 있었다. 건강기능식품 구입 시 고려요인은 부작용(4.72)>복용 후 효과(4.59)>청결도(4.51)>회사신뢰도(4.29) 순으로 나타나 부작용과 복용 후 효과에 대해 중요하게 생각하는 것으로 조사되었다. 이상의 결과를 통해서 건강기능식품에 대한 관심과 섭취의 기회가 증대되고 있는 가운데 식품영양학 분야에 전문가인 영양사조차도 건강기능식품에 대해 건강기능식품에 관한 법률제정 및 시행사실 인식부족 및 건강기능식품의 정확한 인식 및 정보가 부족한 것으로 나타났다. 따라서 영양사의 직무를 올바르게 수행하기 위해서는 다음과 같이 제언하고자 한다. 첫째, 건강기능식품의 정확한 이해가 필요하고 건강기능식품 원료 및 성분에 대한 정확한 분석능력과 그 성분이 인체에 미치는 효능에 관한 최신 연구들의 정확한 정보 확보와 적용이 필요하며 둘째, 건강기능식품은 건강상태 유지 및 질병의 발생 위험을 감소시키는데 기여하므로 식사의 일부로 간주하여 교육을 실시해야하고 셋째, 차후 학교에서 영양상담실을 설치하여 운영할 경우 학생, 일반교사 및 학부모 대상으로 교육을 할 경우 건강기능식품은 질병의 치료 목적으로 사용되는 것이 아님을 주지시키고 전달해야하며 넷째, 건강기능식품에 대한 올바른 인식을 가지고 선택할 수 있도록 교육하기 위해 정보교류 활성화 및 보수교육 등을 통해 영양사의 전문성을 신장하기 위한 많은 노력과 준비가 필요하다고 사료된다.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제27회 학술발표회
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• pp.101-113
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• 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.