1980-'90년대에 쌀의 자급생산이 지속되고 생활수준 향상에 따른 양질미 수요가 점증되면서 자포니카 다수성 품종의 미질 개선에 힘을 크게 기울이게 되었고 식미향상을 위한 육종연구 와 효율적 평가 기술개발 연구에 박차를 가하게 되어 쌀의 이화학적 특성과 식미 및 밥 물리성간 상관과 식미의 객관적 평가방법 개발, 양식미 쌀 및 가공적성 특수미 개발 등 그 동안 많은 연구성과를 올리게 되었다. 1990년대에 밥맛이 매우 좋은 고품질 자포니카 품종과 대립, 심백미, 향미, 유색미 등 가공용 특수미 품종을 개발 보급하였고 식미와 용도에 대하여 식미검정계, 신속점도측정계 및 texture 분석계 등을 이용하여 검토하였다. 최근에는 아밀로스 함량이 9%로 찹쌀과 맵쌀의 중간 성질을 가진 중간찰 품종인 '백진주벼'와 배유가 보얀 멥쌀인 '설갱 벼'를 비롯하여 라이신 함량이 높은 '영안벼' 육성하여 쌀의 가공 이용성과 기능성을 한층 높혀 놓았다. 식미와 여러 가지 미질 특성간 관련성에 관한 연구 결과를 요약해 보면 다음과 같다. 쌀의 상온흡수율 및 최대흡수율은 K/Mg율 및 알칼리 붕괴도와 유의한 부의 상관성을 나타내었으며 가열흡수율이 높은 품종일수록 밥의 용적팽창률이 컸다. 수분함량이 낮은 경질인 쌀일수록 침지 20분 후의 상온흡수율과 최대흡수율이 더 높은 경향이었으며 이러한 흡수 특성은 쌀 단백질 함량이나 아밀로스 함량 및 식미와는 유의한 상관성이 없었다 취반 적정가수량은 품종에 따라 마른 쌀 무게의 1.45-l.61배의 변이를 나타내었고 평균은 1.52배였으며 알맞게 취사된 밥의 부피는 평균 쌀 부피의 2.63배가되었다. 쌀 형태, 알칼리붕괴도, 호응집성, 아밀로스 및 단백질 함량은 거의 비슷하지만 식미에 차이가 있는 자포니카 품종들을 사용하여 식미와 관련된 쌀의 이화학적 특성을 검토한 결과, 밥의 윤기와 식미 총평은 생산 연도에 따라 호화점도 특성 중 최고점도, 최저점도 및 응집점도와 밀접한 관계를 나타내었다. 밥맛이 가장 좋은 일품 벼는 쌀의 외층에 아밀로스 함량 분포가 낮고 쌀을 열탕에 담근 20분간 우러난 용출액의 요드 정색도가 낮고 증가정도도 완만하였다. 일품 벼는 밥맛이 떨어지는 동해 벼에 비해 밥알 횡단면의 주사형 전자현미경 사진에서 밥알 외층의 호화전분의 그 물망이 매우 치밀하고 속층의 전분립의 호화정도가 양호하였다. 식미총평은 식미관련 이화학적 특성과의 관계를 이용한 중회귀식에 의해 매우 높은 결정계수로 추정이 가능하였다. 밥노화의 품종간 차이는 α-amylase-iodine 법으로 비교할 수 있었는데 노화정도가 적었던 품종은 일품벼, 추청벼, 사사니시끼, 진부벼 및 고시히까리였다. 통일형 품종인 태백벼와 자포니카 품종 중 섬진벼가 비교적 밥노화가 빨랐다 일반적으로 밥맛이 좋은 품종이 밥의 노화정도가 느렸으며 찬밥의 탄력성이 큰 경향이었다. 또한 밥노화가 느렸던 품종은 최저점도가 높았고 최종점도가 낮았다 찬밥의 탄력성은 쌀의 마그네슘함량과 밥의 용적팽창률과 밀접한 관계를 나타내었다. 식은밥의 더운밥 대비 경도 변화율은 취반용출액의 고형물량과 취반용적 팽창률과 부의 상관을 나타내었다. 식미관련 주요 이화학적 특성은 밥의 노화와도 직접 간접으로 상관이 있는 것으로 평가되었다. 쌀의 여러 가지 식품 가공적성과 관련된 형태 및 이화학적 특성은 가공식품 종류에 따라 매우 다르다. 쌀 튀김성은 호응집성이 연질이거나 아밀로스 함량이 낮을수록 양호하며 지질함량이나 단백질 함량이 높으면 좋지 않은 경향이다. 심복백정도가 심할수록 튀김현미 정립률이 떨어지며 현미 강도가 높을수록 튀김률은 높은 경향이었다 쌀국수는 밀가루와 50% 혼합시에 쌀의 칼륨 및 마그네슘 함량이 높은 품종일수록 제면 총평이 낮은 경향이었고 제면이 양호한 것이 국수물의 용출고형 물량이 적은 경향이었다. 쌀빵 가공적성은 품종에 따라서 현미와 백미간에 현저한 차이를 나타내는 것이 있는데 현미에서 반죽의 부피 증가율이 큰 쌀일수록 푹신한 감이 있는 쌀방 제조가 가능하였으며 백미에서 단백질 함량이 높은 품종일수록 쌀빵이 더욱 촉촉한 느낌이 있는 경향이었다. 아밀로스 함량이 높고 호응집성이 경질인 쌀일수록 쌀빵의 탄력성이 더 높은 경향이었다. 쌀의 발효 및 양조적성은 심복백이 심한 쌀이나 새로운 돌연변이인 뽀얀 멥쌀이 홍국균이나 홍국균의 균사활착 밀도가 높고 당화 효소 역가도 높은 경향이었으며 쌀알이 대립이면서 단백질 함량이 낮은 쪽이 양조에 유리한 것으로 알려져 있다. 찰벼 품종도 여러 가지 이화학적 특성과 전분구조 특성의 차이에 따라 9개의 품종군으로 나누어 볼 수 있을 만큼 품종적 변이가 크며 이들 이화학적 및 구조적 특성간에 상호 밀접한 연관성을 나타내었으며 유과·인절미·식혜ㆍ미숫가루 등에 상당한 가공적성의 차이를 보였다. WTO 체제 출범이후 생산비와 가격 면에서 경쟁력이 약한 우리 쌀이 살아남기 위해서는 품질의 고급화와 쌀 가공식품의 다양화 및 고기능성 개발을 추구할 수밖에 없다. 따라서 이와 같은 노력은 벼 품종개발만으로 소기의 성과를 올리기 어렵고 쌀 식품의 고급화 및 다양화를 위한 여러 분야의 긴밀한 연구협력이 수반되지 않으면 안 된다.
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.