• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.67-75
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• 2009

Purpose: As to the analytical method of data, the various programs in which it is used for the quantitative rating of the Tl-201 myocardial perfusion SPECT are reported that there is a difference. Therefore, the measured value error of the mutual program is expected to be generated even if the quantitative analysis is made against data of the same patient. Using quantitative index that able to represent myocardial perfusion defect level, we aimed to determine correlation among three myocardial perfusion analysis programs 4DM (4DMSPECT), QPS (Quantitative Perfusion SPECT), ECT (Emory Cardiac Toolbox) that be used generally in most departments of Nuclear Medicine. Materials and Methods: We analyzed the 145 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Mediacal Center from December 1th 2008 to February 14th 2008. We sorted as normal group and abnormal group. Normal group consist of 80 patients (Male/Female=38/42, age:$65.1{\pm}9.9$) who have low possibility of cardiovascular disease. And abnormal group consist of 65 patients (Male/Female=45/20, age:$63.0{\pm}8.7$) who were diagnosed cardiovascular disease with reversible perfusion defect or fixed perfusion defect through myocardial perfusion SPECT results. Using the 4DM, QPS, and ECT programs, the total defect extent (TDE) such as LAD, LCX, RCA and the summed stress score (SSS) have been analysed for their correlations and statistical comparison with the paried t-test for the quantitative indices analysed from each group. Results: The correlation of 4DM:QPS, QPS:ECT, ECT:4DM each group result from 145 patients is 0.84, 0.86, 0.82 at SSS, 0.87, 0.84, 0.87 at TDE, and both index showed good correlation. In paired t-test and Bland-Altman analysis results showed no statistically significant difference in the comparison of QPS:ECT at the mean SSS and TDE, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index. The correlation of 4DM:QPS, QPS:ECT, ECT:4DM program results from abnormal group (65 patients) is 0.72, 0.72, 0.70 at SSS and 0.77, 0.70, 0.77 at TDE and TDE and SSS has a good correlation. In abnormal group, paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.89) and TDE (p=0.23) comparison, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). In normal group (80 patients), paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.95) and TDE (p=0.73) comparison. And 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). Conclusions: The perfusion defect of the Tl-201 myocardial perfusion SPECT was analyzed in not only the patient in whom it has the cardiovascular disease but also the patient in whom the possibility of the cardiovascular disease is few. In the comparison of the all group research, the mean of the TDE and SSS, 4DM was lower than QPS and ECT progrms. Each program showed good correlation and the results showed statistically significant difference. However, in this way, it is determined to be compatible about the analysis value in which the large-scale side between the programs uses each program a difference in a clinical in the Bland-Altman analyzed result in spite of the good correlation and cannot use. but, this analyzed result will be able to be usefully used as the reference material for the clinical read and is expected.

• Journal of Chest Surgery
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• v.39 no.5 s.262
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• pp.354-358
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• 2006

Background: Pulsatile pumps for extracorporeal circulation have been known to be better for tissue perfusion than non-pulsatile pumps but be detrimental to blood corpuscles. This study is intended to examine the risks and benefits of $T-PLS^{TM}$ through the comparison of clinical effects of $T-PLS^{TM}$ (pulsatile pump) and $Bio-pump^{TM}$ (non-pulsatile pump) used for coronary bypass surgery. Material and Method: The comparison was made on 40 patients who had coronary bypass using $T-PLS^{TM}\;and\;Bio-pump^{TM}$ (20 patients for each) from April 2003 to June 2005. All of the surgeries were operated on pump beating coronary artery bypass graft using cardiopulmonary extra-corporeal circulation. Risk factors before surgery and the condition during surgery and the results were compared. Result: There was no significant difference in age, gender ratio, and risk factors before surgery such as history of diabetes, hypertension, smoking, obstructive pulmonary disease, coronary infarction, and renal failure between the two groups. Surgery duration, hours of heart-lung machine operation, used shunt and grafted coronary branch were little different between the two groups. The two groups had a similar level of systolic arterial pressure, diastolic arterial pressure and mean arterial pressure, but pulse pressure was measured higher in the group with $T-PLS^{TM}\;(46{\pm}15\;mmHg\;in\;T-PLS^{TM}\;vs\;35{\pm}13\;mmHg\;in\;Bio-pump^{TM},\;p<0.05)$. The $T-PLS^{TM}$-operated patients tended to produce more urine volume during surgery, but the difference was not statistically significant $(9.7{\pm}3.9\;cc/min\;in\;T-PLS^{TM}\;vs\;8.9{\pm}3.6\;cc/min\;in\;Bio-pump^{TM},\;p=0.20)$. There was no significant difference in mean duration of respirator usage and 24-hour blood loss after surgery between the two groups. Plasma free Hb was measured lower in the group with $T-PLS^{TM}\;(24.5{\pm}21.7\;mg/dL\;in\;T-PLS^{TM}\;versus\;46.8{\pm}23.0mg/dL\;in\;Bio-pump^{TM},\;p<0.05)$. There was no significant difference in coronary infarction, arrhythmia, renal failure and morbidity rate of cerebrovascular disease. There was a case of death after surgery (death rate of 5%) in the group tested with $T-PLS^{TM}$, but the death rate was not statistically significant. Conclusion: Coronary bypass was operated with $T-PLS^{TM}$ (Pulsatile flow pump) using a heart-lung machine. There was no unexpected event caused by mechanical error during surgery, and the clinical process of the surgery was the same as the surgery for which $Bio-pump^{TM}$ was used. In addition, $T-PLS^{TM}$ used surgery was found to be less detrimental to blood corpuscles than the pulsatile flow has been known to be. Authors of this study could confirm the safety of $T-PLS^{TM}$.

• Explosives and Blasting
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• v.9 no.1
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• pp.3-13
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• 1991

The cautious blasting works had been used with emulsion explosion electric M/S delay caps. Drill depth was from 3m to 6m with Crawler Drill ${\phi}70mm$ on the calcalious sand stone (soft -modelate -semi hard Rock). The total numbers of test blast were 88. Scale distance were induced 15.52-60.32. It was applied to propagation Law in blasting vibration as follows. Propagtion Law in Blasting Vibration $V=K(\frac{D}{W^b})^n$ were V : Peak partical velocity(cm/sec) D : Distance between explosion and recording sites(m) W : Maximum charge per delay-period of eight milliseconds or more (kg) K : Ground transmission constant, empirically determind on the Rocks, Explosive and drilling pattern ets. b : Charge exponents n : Reduced exponents where the quantity $\frac{D}{W^b}$ is known as the scale distance. Above equation is worked by the U.S Bureau of Mines to determine peak particle velocity. The propagation Law can be catagorized in three groups. Cubic root Scaling charge per delay Square root Scaling of charge per delay Site-specific Scaling of charge Per delay Plots of peak particle velocity versus distoance were made on log-log coordinates. The data are grouped by test and P.P.V. The linear grouping of the data permits their representation by an equation of the form ; $V=K(\frac{D}{W^{\frac{1}{3}})^{-n}$ The value of K(41 or 124) and n(1.41 or 1.66) were determined for each set of data by the method of least squores. Statistical tests showed that a common slope, n, could be used for all data of a given components. Charge and reduction exponents carried out by multiple regressional analysis. It's divided into under loom over loom distance because the frequency is verified by the distance from blast site. Empirical equation of cautious blasting vibration is as follows. Over 30m ------- under l00m ${\cdots\cdots\cdots}{\;}41(D/sqrt[2]{W})^{-1.41}{\;}{\cdots\cdots\cdots\cdots\cdots}{\;}A$ Over 100m ${\cdots\cdots\cdots\cdots\cdots}{\;}121(D/sqrt[3]{W})^{-1.66}{\;}{\cdots\cdots\cdots\cdots\cdots}{\;}B$ where ; V is peak particle velocity In cm / sec D is distance in m and W, maximLlm charge weight per day in kg K value on the above equation has to be more specified for further understaring about the effect of explosives, Rock strength. And Drilling pattern on the vibration levels, it is necessary to carry out more tests.

• Journal of Distribution Research
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• v.17 no.1
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• pp.37-63
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• 2012

In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.

• Tuberculosis and Respiratory Diseases
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• v.43 no.2
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• pp.201-209
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• 1996

Background : Auto-PEEP which develops when expiratory lung emptying is not finished until the beginning of next inspiration is frequently found in patients on mechanical ventilation. Its presence imposes increased risk of barotrauma and hypotension, as well as increased work of breathing (WOB) by adding inspiratory threshold load and/or adversely affecting to inspiratory trigger sensitivity. The aim of this study is to evaluate the relationship of auto-PEEP with WOB and to evaluate the effect of PEEP applied by ventilator (external PEEP) on WOB in patients with auto-PEEP. Method : 15 patients, who required mechanical ventilation for management of acute respiratory failure, were studied. First, the differences in WOB and other indices of respiratory mechanics were examined between 7 patients with auto-PEEP and 8 patients without auto-PEEP. Then, we applied the 3 cm $H_2O$ of external PEEP to patients with auto-PEEP and evaluated its effects on lung mechanics as well as WOB. Indices of respiratory mechanics including tidal volume ($V_T$), repiratory rate, minute ventilation ($V_E$), peak inspiratory flow rate (PIFR), peak expiratory flow rate (PEFR), peak inspiratory pressure (PIP), $T_I/T_{TOT}$, auto-PEEP, dynamic compliance of lung (Cdyn), expiratory airway resistance (RAWe), mean airway resistance (RAWm), $p_{0.1}$, work of breathing performed by patient (WOB), and pressure-time product (PTP) were obtained by CP-100 Pulmonary Monitor (Bicore, USA). The values were expressed as mean $\pm$ SEM (standard error of mean). Results : 1) Comparison of WOB and other indices of respiratory mechanics in patients with and without auto-PEEP : There was significant increase in WOB ($l.71{\pm}0.24$ vs $0.50{\pm}0.19\;J/L$, p=0.007), PTP ($317{\pm}70$ vs $98{\pm}36\;cm$ $H_2O{\cdot}sec/min$, p=0.023), RAWe ($35.6{\pm}5.7$ vs $18.2{\pm}2.3\;cm$ H2O/L/sec, p=0.023), RAWm ($28.8{\pm}2.5$ vs $11.9{\pm}2.0cm$ H2O/L/sec, p=0.001) and $P_{0.1}$ ($6.2{\pm}1.0$ vs 2.9+0.6 cm H2O, p=0.021) in patients with auto-PEEP compared to patients without auto-PEEP. The differences of other indices including $V_T$, PEFR, $V_E$ and $T_I/T_{TOT}$ showed no significance. 2) Effect of 3 cm $H_2O$ external PEEP on respiratory mechanics in patients with auto-PEEP : When 3 cm $H_2O$ of external PEEP was applied, there were significant decrease in WOB ($1.71{\pm}0.24$ vs $1.20{\pm}0.21\;J/L$, p=0.021) and PTP ($317{\pm}70$ vs $231{\pm}55\;cm$ $H_2O{\cdot}sec/min$, p=0.038). RAWm showed a tendency to decrease ($28.8{\pm}2.5$ vs $23.9{\pm}2.1\;cm$ $H_2O$, p=0.051). But PIP was increased with application of 3 cm $H_2O$ of external PEEP ($16{\pm}2$ vs $22{\pm}3\;cm$ $H_2O$, p=0.008). $V_T$, $V_E$, PEFR, $T_I/T_{TOT}$ and Cdyn did not change significantly. Conclusion : The presence of auto-PEEP in mechanically ventilated patients was accompanied with increased WOB performed by patient, and this WOB was decreased by 3 cm $H_2O$ of externally applied PEEP. But, with 3 cm $H_2O$ of external PEEP, increased PIP was noted, implying the importance of close monitoring of the airway pressure during application of external PEEP.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).

• Journal of Sericultural and Entomological Science
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• v.8
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• pp.11-25
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• 1968

Various formulae for estimation of leaf production in mulberry trees were investigated and obtained. Four varieties of mulberry trees were used as the materials, and seven characters namely branch length. branch diameter, node number per branch, total branch weight, branch weight except leaves, leaf weight and leaf area, were studied. The formulae to estimate the leaf yield of mulberry trees are as follows: 1. Varietal differences were appeared in means, variances, standard devitations and standard errors of seven characters studied as shown in table 1. 2. Y$_1$=a$_1$X$_1$${\times}$P$_1$......(l) where Y$_1$ means yield per l0a by branch number and leaf weight determination. a$_1$.........leaf weight per branch. X$_1$.......branch number per plant. P$_1$........plant number per l0a. 3. Y$_2$=(a$_2$${\pm}$S. E.${\times}$X$_2$)+P$_1$.......(2) where Y$_2$ means leaf yield per l0a by branch length and leaf weight determination. a$_2$......leaf weight per meter of branch length. S. E. ......standard error. X$_2$....total branch length per plant. P$_1$........plant number per l0a as written above. 4. Y$_3$=(a$_3$${\pm}$S. E${\times}$X$_3$)${\times}$P$_1$.....(3) where Y$_3$ means of yield per l0a by branch diameter measurement. a$_3$.......leaf weight per 1cm of branch diameter. X$_3$......total branch diameter per plant. 5. Y$_4$=(a$_4$${\pm}$S. E.${\times}$X$_4$)P$_1$......(4) where Y$_4$ means leaf yield per 10a by node number determination. a$_4$.......leaf weight per node X$_4$.....total node number per plant. 6. Y$\sub$5/= {(a$\sub$5/${\pm}$S. E.${\times}$X$_2$)Kv}${\times}$P$_1$.......(5) where Y$\sub$5/ means leaf yield per l0a by branch length and leaf area measurement. a$\sub$5/......leaf area per 1 meter of branch length. K$\sub$v/......leaf weight per 100$\textrm{cm}^2$ of leaf area. 7. Y$\sub$6/={(X$_2$$\div$a$\sub$6/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$......(6) where Y$\sub$6/ means leaf yield estimated by leaf area and branch length measurement. a$\sub$6/......branch length per l00$\textrm{cm}^2$ of leaf area. X$_2$, K$\sub$v/ and P$_1$ are written above. 8. Y$\sub$7/= {(a$\sub$7/${\pm}$S. E. ${\times}$X$_3$)}${\times}$K$\sub$v/${\times}$P$_1$.......(7) where Y$\sub$7/ means leaf yield estimates by branch diameter and leaf area measurement. a$\sub$7/......leaf area per lcm of branch diameter. X$_3$, K$\sub$v/ and P$_1$ are written above. 9. Y$\sub$8/= {(X$_3$$\div$a$\sub$8/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$.......(8) where Y$\sub$8/ means leaf yield estimates by leaf area branch diameter. a$\sub$8/......branch diameter per l00$\textrm{cm}^2$ of leaf area. X$_3$, K$\sub$v/, P$_1$ are written above. 10. Y$\sub$9/= {(a$\sub$9/${\pm}$S. E.${\times}$X$_4$)${\times}$K$\sub$v/}${\times}$P$_1$......(9) where Y$\sub$7/ means leaf yield estimates by node number and leaf measurement. a$\sub$9/......leaf area per node of branch. X$_4$, K$\sub$v/, P$_1$ are written above. 11. Y$\sub$10/= {(X$_4$$\div$a$\sub$10/$\div$S. E.)${\times}$K$\sub$v/}${\times}$P$_1$.......(10) where Y$\sub$10/ means leaf yield estimates by leaf area and node number determination. a$\sub$10/.....node number per l00$\textrm{cm}^2$ of leaf area. X$_4$, K$\sub$v/, P$_1$ are written above. Among many estimation methods. estimation method by the branch is the better than the methods by the measurement of node number and branch diameter. Estimation method, by branch length and leaf area determination, by formulae (6), could be the best method to determine the leaf yield of mulberry trees without destroying the leaves and without weighting the leaves of mulberry trees.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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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.