• 한국식품영양과학회지
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• 제38권6호
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• pp.694-708
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• 2009

본 연구는 서울시에 거주하는 남자 중 고생을 대상으로 흡연과 음주 현황을 조사하여 흡연 유무에 따라 비흡연군 (199명), 흡연군(11명), 흡연 음주군(52명)으로 분류하였으며, 설문을 통해 흡연과 음주습관, 식생활 관련사항 및 영양섭취상태 등을 조사하여 흡연과 음주습관이 식생활 습관, 영양소 섭취상태 및 건강상태에 어떠한 영향을 미치는지를 조사하였다. 전체 조사대상자의 신장 및 체중은 각각 $173.5{\pm}6.8\;kg$, $64.83{\pm}11.8\;cm$로 신장은 한국인 체위 기준치에 비해 1.5 cm 이상 더 컸으며, 체중은 1.0 kg 정도 높게 조사되었고, 전체 조사대상자의 체질량지수의 평균값은 $21.4{\pm}3.7\;kg/m^2$정도로 나타났다. 전체 조사대상자의 89% 이상이 건강에 대해 관심이 있는 것으로 조사되었으며, 43.5%의 청소년이 자신만의 건강유지 비결로 규칙적인 운동을 가장 많이 손꼽았다. 또한 66.0%의 청소년이 실제로 운동을 하고 있었으며, 일주일에 평균 3번 정도한다고 답한 전체 응답자는 37.3%로 가장 많은 비율을 차지하였다. 1회 운동 시 평균 운동시간은 30분${\sim}$1시간 정도가 가장 많은 비율(46.3%)을 차지하였다. 흡연자 63명 중 음주를 하는 학생이 52명 82.5%이었으며, 11명 17.5%만은 흡연만 하여 본 연구결과에서 보면, 흡연을 하는 경우 음주를 동시에 하는 것으로 조사되었다. 55.6%의 남자 중 고생은 중학교 때 흡연을 시작하였으며, 흡연 동기는 38.1%가 호기심으로 시작했다고 답하였다. 특히 배고플 때 흡연욕구를 강하게 느꼈으며, 흡연 장소로는 화장실이라고 답하였다. 흡연기간은 6개월에서 1년 사이가 가장 많은 비율을 차지하였고, 하루 흡연량은 하루에 $5{\sim}10$개피를 핀다고 답한 비율이 가장 많이 차지하였다. 금연을 하기 위해 시도했던 방법으로는 그냥 참은 경우가 69.0%였으며, 금연에 실패한 이유로는 의지부족이 44.4%로 가장 많은 비율을 차지하였다. 금연의 의향을 묻는 질문에는 전체 조사대상자의 87.1%가 금연을 하고 싶다고 답하였으나, 금연 프로그램을 실시하면 참여하겠느냐는 질문에는 단지 56.7%만이 참여하길 원하였다. 흡연 음주군에서 음주 시작 시기는 50.0%가 고등학교 때 시작하였으며, 사교적 필요성에 의해서 음주를 시작하였다고 답하였다. 음주는 주로 지정된 장소에서 하였으며, 조사대상자의 반 이상의 남자 중 고생이 한 번에 마시는 술의 양은 소주 한 병이하라고 답하였다. 음주의 욕구를 강하게 느낄 때는 친구가 술을 먹을 때라고 답하였으며, 금주 의향을 묻는 질문에는 단지 40.4%만이 금주를 하겠다고 답하였다. 34.4%의 아동이 매일 아침식사를 하는 것으로 조사되었으며, 아침식사를 전혀 하지 않는 결식률도 16.4%나 되었다. 아침식사를 거르는 이유로는 47.0%가 '아침시간이 바빠 시간이 없어서'라고 답하였으며, 건강상의 문제를 고려해 볼 때 결식의 방안을 마련하는 것이 시급한 과제라고 사료된다. 과식을 하는 이유로는 전체 조사대상자의 52.5%가 좋아하는 음식이 많아서 과식을 하는 것으로 조사되었으며, 흡연 음주군에서 과식을 자주하는 것으로 나타났다(p<0.05). 간식의 경우 하루에 $1{\sim}2$회 한다는 전체 청소년의 비율이 72.6%를 차지하였으며, 간식으로 섭취하는 식품으로는 비흡연군에서는 빵류 및 감자 40.2%, 패스트푸드 및 튀김식품을 30.7%로 많이 섭취하고 있었으며(p<0.05), 흡연군에서는 탄산음료 및 빙과류를 간식으로 섭취하는 비율이 36.4%나 되었고, 흡연 음주군에서는 과일류(38.5%)와 패스트푸드 및 튀김식품(26.9%)을 간식으로 가장 많이 섭취하는 것으로 조사되었다. 남자 중 고생이 주식으로 섭취하는 탄수화물 식품의 섭취비율 중 비흡연군에서는 다른 군에 비해 잡곡과 현미를 각각 45.7%와 36.2%로 유의하게 높았다(p<0.05). 흡연 음주군에서는 과일을 자주 섭취하는 비율이 9.6%로 매우 낮았으며(p<0.05), 50.0%가 패스트푸드를 섭취하는 것으로 조사되었다. 식생활 평가에서는 흡연군에서 삼겹살, 갈비 등 지방이 많은 육류를 주 2회 이상 먹는 비율이 높았으며, 아이스크림이나 과자, 탄산음료를 주 2회 이상 간식으로 자주 먹는 비율도 54.3%로 유의하게 높았다(p<0.05). 전체적인 영양소 섭취상태는 $15{\sim}19$세 청소년의 영양섭취기준에 제시한 기준치에 비해 현저히 높았으며, 열량 섭취의 경우 비흡연군에 비해 흡연군과 음주 흡연군에서 유의하게 높았다(p<0.05). 특히 흡연 음주군에서는 다른 군에 비해 인이 유의하게 높은 것으로 조사되었으며(p<0.05), 콜레스테롤(p<0.05)과 소디움(p<0.05) 섭취량은 흡연군에서 가장 높았다. 본 연구결과 청소년들이 흡연과 음주를 하게 되는 가장 큰 이유가 친구나 학교 선 후배 등 또래 집단의 영향을 받는 것으로 조사되었으며, 충동적인 호기심에 의해 시작하는 경우가 많아 흡연과 음주가 건강에 미치는 유해성에 대한 인식이 부족하였다. 또한 흡연을 하는 경우 음주를 동시에 하는 남자 중 고생이 82%가 넘는 것으로 나타나 청소년들의 흡연과 음주 실태는 심각한 사회 문제라 할 수 있으며, 식생활 습관도 음주 흡연군에서는 과일 섭취가 낮은 반면 고열량 식품 선호 및 과식을 하는 것으로 조사되었고, 흡연군에서는 육류 및 가공식품등의 섭취가 높았다. 따라서 선행연구(6)에서도 지적했듯이 친구들의 흡연과 음주의 권유를 단호하게 거절할 수 있는 대처방법을 습득시키거나 흡연과 음주의 욕구를 느낄 때의 실질적인 대처 수단 및 금연을 하는 구체적인 방법 등의 현실적인 교육이 필요하다고 사료된다. 흡연과 음주로 인해 발생되는 편식이나 과식 등의 잘못된 식습관을 바로 잡아 청소년의 성장 발달에 도움을 줄 수 있는 균형적인 영양섭취가 중요하며, 영양적인 중요성을 인식할 수 있는 교육도 뒷받침되어야 할 것이다. 또한 청소년 시기에 먹거리의 중요성을 인식시켜 건강에 유익하고, 안전한 식품을 선택할 수 있는 감각을 습득시키는 것도 중요할 것으로 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.