• 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.

• 한국작물학회지
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• 제5권1호
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• pp.1-35
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• 1969

I. 강원도 춘천지방에서 주로 재배되는 수도 품종 20개를 공시재배하여 출수기에 신장절위(간선단으로부터 4 개절위)경엽에 대한 형태조사를 한 바 그 결과는 다음과 같다. 1. 공시한 20 개품종의 평균엽면적은 제 1 엽 18.61 $cm^2$, 제 2 엽 21.84 $cm^2$, 제 3 엽 21.52 $cm^2$, 제 4 엽 18.56 $cm^2$로서 제 2 엽과 제 3 엽이 크고 제 1 엽과 제 4 엽이 작았으며 엽신중은 제 1 엽 97.0 mg, 제 2 엽 118.1 mg, 제 3 엽 115.4mg, 제 4 엽 95.3 mg로서 엽위별의 비중은 전기 엽면적의 경우와 같았고 엽초중(절당)은 제 1 엽초 176.3 mg, 제 2 엽초 163.7 mg, 제 3 엽초 163.4 mg, 제 4 엽초 123.9 mg 로서 절위가 상승함에 따라 현저히 컸다. 또한 엽중(엽신+엽초)은 절위당 제 1 엽 273.3 mg, 제 2 엽 281.8 mg, 제 3 엽 278.8 mg, 제 3 엽 219.1 mg로서 엽위별의 제2 및 제3엽이 컸고 제1엽과 제4엽이 적었으며 고간중(엽신+엽초+간)은 제1절위 374.4 mg, 제2절위 418.2 mg, 제3절위 446.1mg 및 제4절위 362.1 mg로서 역시 중간절위가 크고 상위와 하위에서 적었다. 2. 신장 절위 경엽의 품종간 변이계수를 산출하여 본즉 엽면적에 있어서 12.75%, 엽신중 15.29%, 엽초중 15.90%, 절간중 11.42%, 엽중(엽신중+엽초중) 15.45% 그리고 엽중 13.24%였으며 각절위에 있어서 엽면적, 엽신, 엽초, 간중등의 품종간 변이계수는 간중을 제외하고는 모두 제2 및 제3절위에 있어서 작았고 제3 및 제4절위에서 컸으며 절간중에 있어서는 제3 및 제4절위의 변이계수가 작았다. 그리고 각 절위간 경엽의 변이계수는 엽면적 8.90%로서 가장 적고 엽신중 11.24%로서 적은 편이었으며 간중은 20.02%로서 가장 컸다. 3. 수도의 엽간중에 대하여 엽신 엽초 및 절간이 각각의 고유하는 중량의 비율은 29.2%로서 가장 높고 간중 34.2%, 엽신중 26.6%로서 낮으며 각 절위에 있어서도 각 부분이 점유하는 비율은 대체로 같은 경향이지만 엽신중의 비중은 제2엽에서 크고 엽초중은 상위절에 갈수록 간중은 반대로 하위절일수록 비중이 높았다. 4. 4개절위를 합한 엽신/간비는 77.7%, 엽초/간비 114.5%, 엽신/엽초비 67.9% 및 엽신/간+엽초비 36.2%이며 상위벌에서 엽신/간비 및 엽초/간비는 높고 엽신/엽초비는 하위절에서 높았다. 5. 수도의 신장 절위 경엽과 수량과의 관계는 전체엽면적의 4개엽과 정조수량과의 상관관계(r)는 0.666이었고 제1엽 및 제2엽은 0.659 및 0.609로서 각각 고도의 정(+)의 상관을 보였으며 제3엽과 제4엽은 0.464, 0.523으로서 유의상관을 인정하였으며 전체엽신중과 정조수량간에는 0.678, 제1엽 0.691, 제2엽 0.654, 제3엽 0.570으로서 각각 고도의 정(+)의 상관을 보였고 제4엽에 있어서는 0.544로서 유의상관을 보였으며 엽중(엽신중+엽초중)과 수량과의 상관은 엽면적의 경우와 동일한 경향을 보였고 엽간중과 수량과의 상관은 총고간중 및 제1절위고간중에서만 고도의 정(+)상관을 보였고 그 밑에 절위에서는 유의상관을 보였다. 한편 전체엽초중과 정조수량간에 있어서는 0.572, 제1엽초 0.623으로서 각각 고도의 정(+)상관을 보였고 제2, 제3 및 제4엽초에 있어서는 각각 0.486, 0.513 및 0.450 으로서 유의상관을 보였고 간중과 정조수량과의 상관은 모두 0.377 이하로서 낮은 상관을 보였다. 6. 수량계급에 따르는 품종들의 신장 절위 경엽의 평균치로 본 엽신중, 엽면적 1 $cm^2$당 엽신중, 엽초중, 간중, 엽중은 모두 다수품종에서 컸으며 중수>소수품종의 순위로 낮았고 각 절위별에 있어서도 대체로 같은 경향을 보였으며 그들에 있어서의 절위간변이는 엽면적, 엽신중, 엽초중, 간중 모두 다수품종에서는 현저히 적고 중수 및 소수품종에서 컸다. 7. 수량계급에 따르는 품종들의 식물체지상부 구성비율 즉, 엽신, 엽초, 엽간이 각각 점유하는 비율은 다수품종에 있어서 엽신 27.6% 엽초 39.5%, 간 32.9%인데 비하여 소수품종은 엽신 25.5%, 엽초 38.1%, 간 36.4%이고 중수품종은 그들 중간적 값을 보였다. 8. 수량계급에 따르는 품종들의 엽신/간비, 엽초/간비는 다수품종은 높고 소수품종은 낮았으며 중수품종은 그들 중간을 보였다. II. 수도품종 신 2 호, 시로가네 및 진흥의 3개를 공시하여 10a당 실소를 8kg, 12kg 및 16kg의 3개수준으로 시용하여 신장 절위 경엽의 형태변이를 조사하는 한편 잎의 실소함량을 분석하여 그들과 수량과의 관계를 살펴 본 결과는 다음과 같다. 1. 신장 절위 경엽의 시용량에 따르는 3개 품종의 평균형태변이치는 총엽면적(선단으로부터 4개엽 총합)은 실소 8kg구에 비하여 2배비에서 16.5%의 증대를 보였으며, 총엽신중에 있어서도 거의 같은 비율의 증대를 각각 보였고, 총엽초중에 있어서는 2개비구 7.8%, 1.5 개비구 4.9%의 증대를 보였고, 각엽위별에 있어서도 전자와 비슷한 경향을 보였다. 한편 간중은 반대로 2개비구 11.2%, 1.5개비구 1.5%씩 각각 감소되었으며, 그 정도는 특히 하위절위에서 현저하였다. 2. 각 품종의 신장 절위 경엽이 시비량에 따르는 변이계수는 총엽면적에 있어서는 제002 15.40%, 시로가네 12.87% 및 진흥 10.99%였고, 각 절위별로 엽신의 변이가 큰 것은 신 002는 제4엽, 시로가네는 제2엽, 진흥은 제1엽으로서 품종간에 차이가 있었다. 총엽신중의 변이계수는 총엽면적의 경우와 같은 경향을 보였고, 총간중의 변이계수는 제002 7.72%, 시로가네 12.11% 및 진흥 0,94% 이였으며 각 절위별 변이의 정도도 품종에 따라 다르다. 3. 신장 절위 경엽의 시비량에 따르는 절위간 변이는 엽면적, 엽신중, 간중 모두 N8kg인 소비조건에서 변이가 크고 N16kg인 다비조건에서 적어졌으며 N12kg구는 이들 중간이고, 엽초중에 있어서는 다비조건에서 변이가 컸다. 4. 엽초중을 구성하는 엽신 엽초 및 절간이 각각 점유하는 비율은 시비량에 따라 다르며, 시비량의 증가에 따라 엽신중의 비율은 현저히 높아지고, 엽초중의 비율은 반대로 낮아진다. 5. 시비량에 따르는 공시품종의 신장 절위 경엽의 상호관계를 보면 엽신/간비는 소비구에서 낮고, 엽신/엽초비는 시비량의 증가에 따라 낮아지며, 엽초/간비는 높아지고 엽신/간십엽초중비율은 낮아졌다. 6. 시비량의 증가에 따라 공시품종 모두 신장절위의 엽면적, 엽신중, 엽초중은 증대되었다. 그에 따라 수량도 증가하는 경향을 보이고, 간중은 시비량 증가에 따라 감소되고 수량은 반대로 증대되는 경향을 보이는데 그 정도는 품종에 따라 차이가 있다. 7. 출수기 및 성숙기에 있어서의 잎의 실소함량은 시비량에 따라 다르며, 공시품종 평균실소함량은 출수기에 있어서 N8kg 시비구 2.74%, N12kg 시비구 2.49는 각구 0.80%, 0.92% 및 1.03%로서 증비에 의하여 실소함량이 현저히 증가되고 있으며, 엽위별에 있어서는 상위엽일수록 높았다. 8. 잎의 실소 함량은 품종간에 차이가 있는데, 동일품종내에서는 출수기와 성숙기에 있어서 그 함량이 높을수록 수량이 증대되였다.