• Information Systems Review
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• v.19 no.4
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• pp.43-62
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• 2017

This study investigates the effect of bonding and bridging social capital on the technological innovation performance in the Pangyo Techno Valley. In particular, we consider the information technology (IT) usage in industrial cluster as an antecedent of social capital. IT instigates the intra and extra communication and information sharing between employees, thereby promoting the formation of a network of various members. Results show that the IT usage factor positively affects both bridging and bonding social capital, but an evident difference exists among the effects of social capital on the technological innovation performance. In case of Pangyo industrial cluster, bridging social capital exerts significant effect on the technological innovation performance, whereas bonding social capital shows insignificance. Bridging social capital is composed of the interactions of various networks. Bonding social capital is based on the strong tie from trust and internal cooperation. Results are related with the characteristics of Pangyo Techno Valley, where various IT ventures need active communication and information sharing with other organizations for technological innovation performance.

• IMMUNE NETWORK
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• v.20 no.5
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• pp.41.1-41.11
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• 2020

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is a positive-sense single-stranded RNA (+ssRNA) that causes coronavirus disease 2019 (COVID-19). The viral genome encodes twelve genes for viral replication and infection. The third open reading frame is the spike (S) gene that encodes for the spike glycoprotein interacting with specific cell surface receptor - angiotensin converting enzyme 2 (ACE2) - on the host cell membrane. Most recent studies identified a single point mutation in S gene. A single point mutation in S gene leading to an amino acid substitution at codon 614 from an aspartic acid 614 into glycine (D614G) resulted in greater infectivity compared to the wild type SARS-CoV2. We were interested in investigating the mutation region of S gene of SARS-CoV2 from Korean COVID-19 patients. New mutation sites were found in the critical receptor binding domain (RBD) of S gene, which is adjacent to the aforementioned D614G mutation residue. This specific sequence data demonstrated the active progression of SARS-CoV2 by mutations in the RBD of S gene. The sequence information of new mutations is critical to the development of recombinant SARS-CoV2 spike antigens, which may be required to improve and advance the strategy against a wide range of possible SARS-CoV2 mutations.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.4
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• pp.81-97
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• 2019

The purpose of this study is to examine what people in Korea recognize the cultural symbolism and the planting patterns of 'Guǐmok', pagoda tree(Sophora japonica). The species planted in the 'Guǐjeong' was empirically investigated and analyzed to determine which species of pagoda tree or Zelkova tree(Zelkova serrata) was taken through literature surveys, field surveys, and interviews with persons. This 'Guǐjeong' was combined to track how the culture of the 'Guǐmok' planting introduced in China was ultimately accepted and transformed in Korea. In this study, we tried to analyze the meaning implicit in the mystery while checking the distribution of the mystery and the form of the mystery, the name of the pavilion and its relevance to the contrast medium. Essentially, the trees that govern the characterization of the nectar plant, regardless of the region, are considered to be a pagoda tree, which is considered an internal factor in which the pagoda tree culture was not completely transformed into a zelkova tree. It was recognized throughout the Joseon Dynasty that the species representing 'Prime ministers(三公)' was judged from all the Joseon Dynasty periods, based on the builder of Guǐjeong's Aho(雅號) and Dangho(堂號). It was confirmed that the tree was very likely to be planted in place of the painting tree. But now is selectively zelkova tree is in accordance with the preparation of planting site conditions and areas on behalf of the Change is very high probability that is planted. Cultural variables that led to the cultural transformation of the 'Guǐmok' seem to have been deeply involved in the geographical space of China and Korea, Confucian practices of the Choson society, comings and goings and letter bridge, and network strength with China through the book spread. In addition, the culture of 'Guǐmok' is presumed to have led to cultural custom of the upper class, not the whole class, in the Yeongnam region, it can be said that the independent adaptation to act to recognize 'Guǐmok' as a pagoda tree, that is a Sophora japonica, has occurred very strongly. The difference between the cultural areas of Yeongnam and non-Yeongnam is also considered to be an internal factor that has played a major role in the cultural transformation of planting of 'Guǐmok'.

• Journal of Technology Innovation
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• v.30 no.2
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• pp.29-58
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• 2022

Startups lack resources and manpower to build internal capabilities to strengthen market competitiveness; external cooperation such as joint research and networking plays is important. In this study, we analyzed the effect of startups' industry-university cooperation on organizational learning capability and innovation performance. Empirical results demonstrate the mechanism by which government R&D investment strengthens organizational learning capability and creates innovative results by promoting cooperation between startups and universities. First, industry-university cooperation strengthened organizational learning capability. An empirical analysis shows that startups increase internal capabilities through external cooperation. Second, startups' organizational learning capability had a significant effect on innovation performance. We analyze how organizations with high learning capabilities positively develop corporate innovation performance by having a culture of discovery and sharing new ideas. Finally, industry-university cooperation had different effects on organizational learning capability and innovation performance according to the previous experiences of startup founders. In particular, small- and medium-sized (startup) businesses and individual-based experience groups positively affected the creation of organizational learning capabilities and innovation performance through industry-university cooperation. Small- and medium-sized businesses and individual founders have a relatively small cooperative network with the outside world compared to founders of large companies, universities, and research institutes; therefore, they strengthen organizational learning capabilities through cooperation with universities. This study demonstrates that government should create policy inducements for cooperation with universities to maximize the R&D performance of startups. Criticism exists that lending support to startups and universities will hinder innovation performance; nevertheless, government investment plays a role in expanding intangible resources such as accumulating technologies, fostering high-quality human resources, and strengthening innovation networks. Therefore, the government should appropriately utilize the its authority to strengthen investment strategies for startup growth.

• The Journal of Internal Korean Medicine
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• v.44 no.4
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• pp.726-740
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• 2023

Objectives: This review investigated the research on warm herbal foot bath therapy (WHFT) for insomnia. Methods: A search was conducted on the China National Knowledge Infrastructure (CNKI) database to collect relevant studies published up to August 29, 2023. Randomized controlled trials (RCTs) comparing WHFT and sleeping pills in patients with insomnia were included. The methodological quality of the included studies was assessed using the Cochrane risk-of-bias assessment tool. The results of the meta-analysis were presented as risk ratios (RRs) or mean differences (MDs) and their 95% confidence intervals (CIs). Results: A total of 11 RCTs were included. WHFT as monotherapy resulted in a significantly higher total effective rate (TER) (RR, 1.25; 95% CI, 1.15 to 1.36; I²=25%) and an improved Pittsburgh Sleep Quality Index (PSQI) global sore (MD, -3.10; 95% CI, -4.24 to -1.95; I²=73%) compared to benzodiazepines. Additionally, WHFT as a combined therapy with benzodiazepines resulted in a significantly higher TER (RR, 1.15; 95% CI, 1.04 to 1.27; I²=0%) and an improved PSQI global score (MD, -2.23; 95% CI, -4.09 to -0.38; I²=80%) compared to benzodiazepines alone. In network analysis visualizing the components of HWFT, four clusters were discovered, and Polygoni Multiflori Ramuls and Ziziphi Spinosae Semen were the key herbs used in WHFT. Overall, the methodological quality of the included studies was poor. Conclusions: There was limited evidence that WHFT as a monotherapy or combined therapy was effective in improving insomnia. The findings can be used as basic data for future WHFT research in South Korea.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.3
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• pp.1-16
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• 2012

Under the international business circumstance, global supply chain management is considered a vital strategic challenge to small and medium-sized enterprises(SMEs) suffering from deficient resources and capabilities to exploit overseas markets comparing with large corporations. That is because they can expand their business domains into overseas markets by establishing strategic alliances with global supply chain partners. Although a wide range of previous researches have emphasized the cooperative networks in the chain, most are ignoring the importance of developing relational characteristics such as trust and reciprocity with the partners. Besides, verifying the relational factors influencing firms' export performances, some studies proposed different and inconsistent factors. According to the social capital theory, which is the social quality and networks facilitating close cooperation of inter-individual and inter-organization, provides the integrated view to identify the relational characteristics in the aspects of network, trust and reciprocal norm. Meanwhile, a number of researchers shows that global entrepreneurship is the internal and intangible resource necessary to promote SMEs' internationalization. Upon closer examination, however, they cannot explain clearly its influencing mechanism in the inter-firm cooperative relationships. This study is to verify the effect of social capital accumulated within global supply chain on SMEs' qualitative and quantitative export performance. In addition, we shed new light on global entrepreneurship expected to be concerned with the formation of social capital and the enhancement of export performances. For this purpose, the questionnaires, developed through literature review, were collected from 192 Korean SMEs affiliated in Korean Medium Industries Association and Global Chief Executive Officer's Club focusing on their memberships' international business. As a result of multi-regression analysis, the social capital - network, trust and reciprocal norm shared with global supply chain partner - as well as global entrepreneurship - innovativeness, proactiveness and risk-taking - have positive effect on SMEs' export performances. Also global entrepreneurship affects positively social capital which has mediating effect partially in the relationship between global entrepreneurship and performances. These results means that there is a structural process - global entrepreneurship(input), social capital(output), and export performances(outcome). In other words, a firm should consistently invest in and develop the social capital with global supply chain partners in order to achieve common goals, establish strategic collaborations and obtain long-term export performances. Furthermore, it is required to foster the global entrepreneurship in an organization so as to build up the social capital. More detailed practical issues and discussion are made in the conclusion.

• Journal of Hospice and Palliative Care
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• v.16 no.4
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• pp.205-215
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• 2013

Most terminally ill cancer patients experience various physical and psychological symptoms during their illness. In addition to pain, they commonly suffer from fatigue, anorexia-cachexia syndrome, nausea, vomiting and dyspnea. In this paper, I reviewed some of the common non-pain symptoms in terminally ill cancer patients, based on the National Comprehensive Cancer Network (NCCN) guidelines to better understand and treat cancer patients. Cancer-related fatigue (CRF) is a common symptom in terminally ill cancer patients. There are reversible causes of fatigue, which include anemia, sleep disturbance, malnutrition, pain, depression and anxiety, medical comorbidities, hyperthyroidism and hypogonadism. Energy conservation and education are recommended as central management for CRF. Corticosteroid and psychostimulants can be used as well. The anorexia and cachexia syndrome has reversible causes and should be managed. It includes stomatitis, constipation and uncontrolled severe symptoms such as pain or dyspnea, delirium, nausea/vomiting, depression and gastroparesis. To manage the syndrome, it is important to provide emotional support and inform the patient and family of the natural history of the disease. Megesteol acetate, dronabinol and corticosteroid can be helpful. Nausea and vomiting will occur by potentially reversible causes including drug consumption, uremia, infection, anxiety, constipation, gastric irritation and proximal gastrointestinal obstruction. Metoclopramide, haloperidol, olanzapine and ondansetron can be used to manage nausea and vomiting. Dyspnea is common even in terminally ill cancer patients without lung disease. Opioids are effective for symptomatic management of dyspnea. To improve the quality of life for terminally ill cancer patients, we should try to ameliorate these symptoms by paying more attention to patients and understanding of management principles.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• 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.