• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.4
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• pp.189-213
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• 1980

1. Aiming at supply of basic informations on tree species siting and forest fertilization by understanding of soil properties that are demanded by each tree species through studies of forest soil's morphological, physical and chemical properties in relation to tree growth in our country, the necessary data have been collected in the last 10 years, are quantified according to quantification theory and are analyzed in accordance with multi-variate analysis. 2. Test species, larch and the Korean white pine, are plantable in extensive areas from mid to north in the temperate zone and are the two most recommended reforestation tree species in Korea. However, their respective site demands are not known and they have been in confusion or considered demanding the same site during reforestation. When the Korean white pine is planted in larch sites, it has shown relatively good growth. But, when larch is planted in the Korean white pine site it can be hardly said that the larch growth is good. To understand on such a difference soil factors have been studied so as to see how the soil's morphological, physical and chemical factors affect tree growth helped with the electronic computer. 3. All the stands examined are man-made mature forests. From 294 larch plots and 259 white pine plots dominant trees are cut as samples and through stem analysis site index is determined. For each site index soil profiles are made in the related forest-land for analysis. Soil samples are taken from each profile horizon and forest-land productivity classification tables are worked out through physical and chemical analysis of the soil samples for each tree species for the study of relationships between physical, chemical and the combined physical/chemical properties of soil and tree growth. 4. In the study of relationships between physical properties of soil and tree growth it is found out that larch growth is influenced by the following factors in the order of deposit form, soil depth, soil moisture, altitude, relief, soil type, depth of A-horizon, soil consistency content of organic matter soil texture bed rock gravel content aspect and slope. For the Korean white pine the influencing factors' order is soil type, soil consistency bed rock aspect depth of A-horizon soil moisture altitude relief deposit form soil depth soil texture gravel content and slope. 5. In the study of relationships between chemical properties of soil and tree growth it is found out that larch growth is influenced by the following factors in the order of base saturation organic matter CaO C/N ratio, effective $P_2O_5$ PH.exchangeable $K_2O$ T-N MgO C E C Total Base and Na. For the Korean white pine the influencing factors' order is effective $P_2O_5$ Total Base T-N Na C/N ratio PH CaO base saturation organic matter exchangeable $K_2O$ C E C and MgO. 6. In the study of relationships between the combined physical and chemical properties of soil and tree growth it is found out that larch growth is influenced by the following factors in the order of soil depth deposit form soil moisture PH relief soil type altitude T-N soil consistency effective $P_2O_5$ soil texture depth of A-horizon Total Base exchangeable $K_2O$ and base saturation. For the Korean white pine the influencing factors' order is soil type soil consistency aspect effective $P_2O_5$ depth of A-horizon exchangeable $K_2O$ soil moisture Total Base altitude soil depth base saturation relief T-N C/N ratio and deposit from. 7. In the multiple regression of forest soil's physical properties larch's correlation coefficient is 0.9272 and for the Korean white pine it is 0.8996. With chemical properties larch has 0.7474 and the Korean white pine has 0.7365. So, the soil's physical properties are found out more closely related with tree growth than chemical properties. However, this seems due to inadequate expression of soil's chemical factors and it is proved that the chemical properties are not less important than the physical properties. In the multiple regression of the combined physical and chemical properties consisting of important morphological and physical factors as well as chemical factors of forest soils larch's multiple correlation coefficient is found out to be 0.9434 and for the Korean white pine it is 0.9103 leading to the highest correlation. 8. As shown in the partial correlation coefficients larch needs deeper soil depth than the Korean white pine and in the deposit form colluvial and creeping soils are demanded by the larch. Adequately moist to too moist should be soil moisture and PH should be from 5.5 to 6.1 for the larch. Demands of T-N soil texture and soil nutrients are higher for the larch than the Korean white pine. Thus, soil depth, deposit form, relief soil moisture PH N altitude and soil texture are good indicators for species sitings with larch and the Korean white pine while soil type and soil consistency are indicative only limitedly of species sitings due to their wide variation as plantation environments. For larch siting soil depth deposit form relief soil moisture PH soil type N and soil texture are indicators of good growth and for Korean white pine they are soil type soil consistency effective $P_2O_5$ and exchangeable $K_2O$, which is demanded more by the Korean white pine than larch generally. 9. Physical properties of soil has been known as affecting tree growth to greatest extent so far. However, as a result of this study it is proved through computer analysis that chemical properties of soil are not less important factors for tree growth than chemical properties and site demands for larch and the Korean white pine that have been uncertain So far could be clarified.

• Korean Journal of Heritage: History & Science
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• v.45 no.2
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• pp.20-37
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• 2012

After withdrawal of military troops of Chinese Tang dynasty in the 18th year of King Moon-moo's reign(678), the Silla Kingdom had actually unified the Korean peninsula and had divided the territory into 9 states benchmarking the China's local administrations adjustment system. He had established local administrative units by deploying secondary capitals, counties and prefectures in the nine states. The so-called "9 Provinces and 5 Secondary capitals" are what constitutes the local administrations system. The provinces can be compared to current provinces of the Republic of Korea(hereinafter Korea), and secondary capitals to megalopolises. According to a chapter of the Samkuksaki(三?史記) which had recorded the achievements of king Kyoungdeok in December in his 16th year on the throne(757), the local administrative units had amounted to 5 secondary capitals, 117 counties and 293 prefectures. There are still lots of ambiguous points since there have never been any consultation on locations of provinces and secondary capitals' castles, and on structures of cities because the researches for local cities inside the 9 Provinces and 5 Secondary capitals in the Unified Silla Kingdom has been conducted centering on the historic literatures only. The research for restoring structures of cities seen from an archeological perspective are limited to the studies of Taewoo Park("A study on the local cities in the Unified Kingdom Age" 1987) and that of the author("A study on the restoration of planned cities for the Unified Silla Kingdom in terms of the structures and realities of the castles in the 9 Provinces and 5 Secondary capitals" 2009). The Gangneung city of Gangwon province was originally called Haseoryang(河西良) of the Gogureo Kingdom as an ancient nation of Ye(濊). According to "Samkuksaki", it had evolved from Haseoju(河西州) to a secondary capitals in the 8th year of King Seonduk(639). Afterwards, it had been renamed as Myeongju(溟洲) in the 16th year of King Kyoungduk(757), and then several other names were given to it after Goryo dynasty. Taewoo Park claims that it is being defined as a sanctuary remaining in Myoungjudong because of the vestige of bare castle, and this cannot be ascertained due to the on-going urbanization processes. Also, the Kwandong university authority is suggesting an opinion of regarding Myeongju mountain castle located 3 Kms southwest of the center of Gangwon city as commanding post for the pertinent state. The author has restored the pertinent area into a city composed of villages within a lattice framework like Silla Keumkyoung and many other cities. The structure is depicted next. The downtown of Gangneung is situated on a flat terrain at the west bank of Namdaecheon stream flowing southwest to northeast along the inner area of the city. Though there isn't any hill comparatively higher than others in the vicinity, hills are continuously linked east to west along the northern area of the downtown, and the maximum width of flat terrain is about 1 Km and is not so large. Currently, urbanization is being proceeded into the inner portion of Gangneung city, the lands in all directions from the hub of Gangneung station have been readjusted, and thus previous land-zoning program is almost nullified. However, referring to the topographic chart drawn at the time of Japanese colonial rule, it can be validated that land-zoning program to accord the lattice framework with the length of its one side equaling to 190m leaves its vestige about 0.8Km northwest to southeast and about 1.7Km northeast to southwest of the vicinity of Okcheondong, Imdangdong, Geumhakdong, Myeongjudong, and etcetera which comprize the hub of the downtown. The land-zoning vestige within the lattice framework, compared to other cases related with the '9 states and 5 secondary capitals', is very much likely to be that of the Unified Silla Kingdom. That the length of a side of a lattice framework is 190m as opposed to that of Silla Geumkyoung and other cities with their 140m or 160m long sides is a single survey item in the future. The baseline direction for zoning the lands is tilting approximately 37.5 degrees west of northwest to southeast axis in accordance with the topographic features. It seems that this phenomenon takes place because of the direction of Namdaecheon and the geographic constraints of the hills in the north. Reviewing minimally, a rectangular size of zoned land by 4 Pangs(坊) on the northwest to southeast side multiplied by 7 Pangs(坊) on the northeast to southwest side had been restored within a lattice framework. Otherwise, considering the extent of expansion of the existing zoned lands in the lattice framework and one more Pang(坊) being added to each side, it is likely that the size could have been with 5 Pangs(坊) on the northwest to southeast side multiplied by 8 Pangs(坊) on the northeast to southwest side(950 M on the northwest to southeast side multiplied by 1,520m on the northeast to southwest side). The overall shape is rectangle, but land-zoning programs reminiscent of rebuilt roads(red phoenix road) like Jang-an castle(長安城) of Chinese Tang dynasty or Pyoungseong castle(平城城) in Japan is not to be validated. There are some historic items among the roof tiles and earthen wares excavated at local administrative office sites or Gangneung's town castle in Joseon dynasty inside the area assumed to be containing municipal vestiges even though archeological survey for the vestige of Myeongju has not been made yet, and these items deserve dating back to the Unified Silla Kingdom age. Also, all of the construction sites at local administrative authorities of the Joseon dynasty are showing large degrees of slant in the azimuth. This is a circumstantial evidence indicating the fact that the inherited land-zoning programs to be seen in Gangneung in terms of the lattice framework had ever existed in the past. Also, the author does not decline that Myeongju mountain castle had once been the commanding post when reviewing the roof tiles at the edge of eaves in this stronghold. The ancient municipal castles in the Korean peninsula are composed of castles on the flat terrain as well as hilly areas and the cluster of strongholds like Myounghwal, Namhan, Seohyoung mountain castles built around municipal castle of Geumkyoung based on a lattice framework program. Considering that mountain castles are spread in the vicinity of municipal vestiges in other cities other than the 9 states and 5 secondary capitals, it is estimated that Myeongju was assuming the function of commanding post incorporating cities on the flat terrain and castles on the hills.

• Journal of Korean Society of Forest Science
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• v.53 no.1
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• pp.1-26
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• 1981

1. Aiming at supply of basic informations on tree species siting and forest fertilization by understanding of soil properties that are demanded by each tree species through studies of forest soil's morphological, physical and chemical properties in relation to tree growth in our country, the necessary data have been collected in the last 10 years, are quantified according to quantification theory and are analyzed in sccordance with multi-variate analysis. 2. Test species, japanese larch (Larix leptolepis Gord) and the Korean white pine, (pinus koraiensis S et Z.) are plantable in extensive areas from mid to north in the temperate forest zone and are the two most recommended reforestation tree species in Korea. However, their respective site demands are little known and they have been in confusion or considered demanding the same site during reforestation. When the Korean white pine is planted in larch sites, it has shown relatively good growth, but, when Japanese larch is planted in Korean white pine site it can be hardly said that the Japanese Larch growth is good. To understand on such a difference soil factors have been studied so as to see how th soil's morphological, physical and chemical factors affect tree growth helped with the electronic computer. 3. All the stands examined are man-made mature forests. From 294 Japanese larch plots and 259 Korean white pine plots dominant trees are cut as samples and through stem analysis site index is determined. For each site index soil profiles are made in the related forest-land for analysis. Soil samples are taken from each profile horizon and forest-land productivity classification tables are worked out through physical and chemical analyses of the soil samples for each tree species for the study of relationships between physical, chemical and the combined physical/properties of soil and tree growth. 4. In the study of relationships between physical properties of soil and tree growth it is found out that Japanese larch growth is influenced by the following factors in the decreasing order of weight deposit form, soil depth, soil moisture, altitude, relief, soil type, depth a A-horizon, soil consistency, content of organic matter, soil texture, bed rock, gravel content, aspect and slope. For the Korean white pine the influencing factors' order is soil type, soil consistency, bed rock, aspect, depth of A-horizon, soil moisture, altitude, relief, deposit form, soil depth, soil texture, gravel content and slope. 5. In the study of relationships between chemical properties of soil and tree growth it is found out that Japanese larch growth is influenced by the following factors in the order of base saturation, organic matter, CaO, C/N ratio, effective $P_2O_5$, PH, exchangeable, $K_2O$, T-N, MgO, CEC, Total Base and Na. For the Korean white pine the influencing factors' order is effective $P_2O_5$, Total Base, T-N, Na, C/N ratio, PH, CaO, base saturation, organic matter, exchangeable $K_2O$, CEC and MgO. 6. In the study of relationships between the combined physical and chemical properties of soil and tree growth it is found out that Japanese larch growth is influenced by the following factors in the order of soil depth, deposit form, soil moisture, PH, relief, soil type altitude, T-N, soil consistency, effective $P_2O_5$, soil texture, depth of A-horizon, Total Base, exchangeable $K_2O$ and base saturation. For the Korean white pine the influencing factors' order is soil type, soil consistency, aspect, effective $P_2O_5$, depth of A-horizon, exchangeable $K_2O$, soil moisture, Total Base, altitude, soil depth, base saturation, relief, T-N, C/N ratio and deposit form. 7. In the multiple correlation of forest soil's physical properties larch's correlation coefficient for Japanese Larch is 0.9272 and for Korean white pine, 0.8996. With chemical properties larch has 0.7474 and Korean white pine has 0.7365. So, the soil's physical properties are found out more closely related with tree growth than chemical properties. However, this seems due to inadequate expression of soil's chemical factors and it is proved that the chemical properities are not less important than the physical properties. In the multiple correlation of the combined physical and chemical properties consisting of important morphological and physical factors as well as chemical factors of forest soils larch's multiple correlation coefficient is found out to be 0.9434 and for Korean white pine it is 0.9103 leading to the highest correlation. 8. As shown in the partial correlation coefficients Japanese larch needs deeper soil depth than Korean white pine and in the deposit form of colluvial and creeping soils are demanded by the larch. Moderately moist to not moist should be soil moisture and PH should be from 5.5 to 6.1 for the larch. Demands of T-N, soil texture and soil nutrients are higher for the larch than the Korean white pine. Thus, soil depth, deposit form, relief, soil moisture, PH, N, altitude and soil texture are good indicators for species sitings with larch and the Korean white pine while soil type and soil consistency are indicative only limitedly of species sitings due to their wide variations as plantation environments. For the larch siting soil depth, deposit form, relief, soil moisture, pH, soil type, N and soil texture are indicators of good growth and for the Korean white pine they are soil type, soil consistency, effective $P_2O_5$ and exchangeable $K_2O$. In soil nutrients larch has been found out demanding more than the Korean white pine except $K_2O$, which is demanded more by the Korean white pine than Japanese larch generally. 9. Physical properties of soil has been known as affecting tree growth to the greatest extent so far. However, as a result of this study it is proved through computer analysis that chemical properties of soil are not less important factors for tree growth than chemical properties and site demands for the Japanese larch and the Korean white pine that have been uncertain so far could be clarified.