• 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.

• Journal of Korean Society of Steel Construction
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• v.23 no.2
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• pp.199-210
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• 2011

The cables in cable-stayed bridges are under high stress and are very sensitive to vibration due to their small section areas compared with other members. Therefore, it is reasonable to evaluate the cable impact factor by taking into account the dynamic effect due to moving-vehicle motion. In this study, the cable impact factors were evaluated via moving-vehicle-load analysis, considering the design parameters, i.e., vehicle weight, cable model, road surface roughness, vehicle speed, longitudinal distance between vehicles. For this purpose, two steel composite cable-stayed bridges with 230- and 540-m main spans were selected. The results of the analysis were then compared with those of the influence line method that is currently being used in design practice. The road surface roughness was randomly generated based on ISO 8608, and the convergence of impact factors according to the number of generated road surfaces was evaluated to improve the reliability of the results. A9-d.o.f. tractor-trailer vehicle was used, and the vehicle motion was derived from Lagrange's equation. 3D finite element models for the selected cable-stayed bridges were constructed with truss elements having equivalent moduli for the cables, and with beam elements for the girders and the pylons. The direct integration method was used for the analysis of the bridge-vehicle interaction, and the analysis was conducted iteratively until the displacement error rate of the bridge was within the specified tolerance. It was acknowledged that the influence line method, which cannot consider the dynamic effect due to moving-vehicle motion, could underestimate the impact factors of the end-cables at the side spans, unlike moving-vehicle-load analysis.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.117-130
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• 2005

In both deteministic user Optimal Traffic Assignment Model (UOTAM) and stochastic UOTAM, travel time, which is a major ccriterion for traffic loading over transportation network, is defined by the sum of link travel time and turn delay at intersections. In this assignment method, drivers actual route perception processes and choice behaviors, which can become main explanatory factors, are not sufficiently considered: therefore may result in biased traffic loading. Even though there have been some efforts in Stochastic UOTAM for reflecting drivers' route perception cost by assuming cumulative distribution function of link travel time, it has not been fundamental fruitions, but some trials based on the unreasonable assumptions of Probit model of truncated travel time distribution function and Logit model of independency of inter-link congestion. The critical reason why deterministic UOTAM have not been able to reflect route perception cost is that the route perception cost has each different value according to each origin, destination, and path connection the origin and destination. Therefore in order to find the optimum route between OD pair, route enumeration problem that all routes connecting an OD pair must be compared is encountered, and it is the critical reason causing computational failure because uncountable number of path may be enumerated as the scale of transportation network become bigger. The purpose of this study is to propose a method to enable UOTAM to reflect route perception cost without route enumeration between an O-D pair. For this purpose, this study defines a link as a least definition of path. Thus since each link can be treated as a path, in two links searching process of the link label based optimum path algorithm, the route enumeration between OD pair can be reduced the scale of finding optimum path to all links. The computational burden of this method is no more than link label based optimum path algorithm. Each different perception cost is embedded as a quantitative value generated by comparing the sub-path from the origin to the searching link and the searched link.

• Journal of the Korean Society of Tobacco Science
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• v.20 no.1
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• pp.131-138
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• 1998

In order to minimize the trial frequency in the new filter cigarette design, we studied the relationship between smoke yield and tipping materials of cigarette. A three levels full factorial design involving filament denier (X1,2.5-3.3d), Porosity of the acetate filter plug wrap (X2, 3,500-16,000CU) and porosity of the tip paper (X3, 400-1,200CU) was used. Three independent factors (Xl, X2, X3) were chosen for their effects on the various responses and the function was expressed in terms of a quadratic polynomial equation, Y : $\beta$o + $\beta$1Xl + $\beta$2X2 + $\beta$3X3 + $\beta$11Xl2 + $\beta$22X22+ $\beta$33X32 + $\beta$12X1X2 + $\beta$13XIX3 $\beta$23X2X3 which measures the linear, quadratic, and interaction effects. Twenty-nine trial numbers were obtained as a results of using a three levels full factorial design and it was analyzed by the multiple regression analysis with backward stepwise in STATISTICA/pc under restricted conditions. Tar yields of the cigarette was affected by porosity of tip paper (0.66), filament denier (0.47) and porosity of plug wrap (0.28) in the decreasing order, and linear effect of tip paper porosity (B3) and filament denier (91) were significant at a level of 0.01($\alpha$). The filament denier and tipping paper porosity interaction F ratio among three factors had a P-value of 0,000041, indicating higher interaction between these factors. Based on the analysis of variance, the model fitted for Tar (Y1) was significant at 5% confidence level and the coefficient of determination (0.96) was the proportion of variability in the data fitted for by the model.

• Horticultural Science & Technology
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• v.33 no.4
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• pp.501-510
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• 2015

In this study, we investigated the onset and release of endo-dormancy under natural conditions by observing bud break characteristics in 'Fuji' apple trees using water cuttings. Through examinations of bud break rate and days to bud break, we found that the endo-dormancy of 'Fuji' apple tree continues for 70 d from 165 to 255 d after full bloom (DAFB), from late October to early January of the following year. In addition, within 20 d of first bud break, based on a final bud break rate of 60% or more, we able to identify the timing of the changeover from para-dormancy to endo-dormancy, and endo-dormancy to eco-dormancy. Analysis of the chilling requirement during the endo-dormancy period revealed that chilling accumulation up to 255 DAFB to release endo-dormancy amounted to 666 and 517 h based on the CH and Utah models, respectively. Observation of internal changes in the bud during endo-dormancy showed that flower bud differentiation begins from mid-July, and t ime of inflorescence o f the disk f lower is a vailable to f ind. The f lower buds subsequently developed slowly but steadily during endo-dormancy and in the following year in February, the developmental stage of each organ had progressed. Moreover, the flower buds of 'Fuji' apples were mostly healthy during the dormancy period, but some exhibited necrosis of flower primordium, due partial cell damage from the formation of ice crystals rather than a direct effect of the low temperature. Flower buds were formed in both the axillary buds of bourse shoots and terminal buds of spurs, but lower bud differentiation was observed for the terminal buds of spurs at rate of about 65% of total buds, which was directly related to the bud size and shoot diameter.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.15-24
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• 2018

Despite the recognition of the need for productivity information and its importance, the feedback of productivity information is not well-established in the construction industry. Effective use of productivity information is required to improve the reliability of construction planning. However, in many cases, on-site productivity information is hardly management effectively, but rather it relies on the experience and/or intuition of project participants. Based on the literature review and expert interviews, the authors recognized that one of the possible solutions is to develop a systematic approach in dealing with productivity information of the construction job-sites. It is required that the new system should not be burdensome to users, purpose-oriented information management, easy-to follow information structure, real-time information feedback, and productivity-related factor recognition. Based on the preliminary investigations, this study proposed a framework for a novel system that facilitate the effective management of construction productivity information. This system has utilized Sketchup software which has good user accessibility by minimizing additional data input and related workload. The proposed system has been designed to input, process, and output the pertinent information through a four-stage process: preparation, input, processing, and output. The inputted construction information is classified into Task Breakdown Structure (TBS) and Material Breakdown Structure (MBS), which are constructed by referring to the contents of the standard specification of building construction, and converted into productivity information. In addition, the converted information is also graphically visualized on the screen, allowing the users to use the productivity information from the job-site. The productivity information management system proposed in this study has been pilot-tested in terms of practical applicability and information availability in the real construction project. Very positive results have been obtained from the usability and the applicability of the system and benefits are expected from the validity test of the system. If the proposed system is used in the planning stage in the construction, the productivity information and the continuous information is accumulated, the expected effectiveness of this study would be conceivably further enhanced.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.347-356
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• 2014

Passenger cars occupy about 74% among registered vehicles in Korea and the ratio of transportation mode sharing is approximately 60% in the passenger transport part. However, there is no statistics related to travel characteristics of passenger cars, and official statistics are estimated from O/D travel data. Thus, National Transportation DataBase Center in KOTI has attempted to construct various statistical data through Korea Vehicle Use Survey. Based on these data, Analysis of Variance (ANOVA) was conducted to investigate the differences in travel characteristics of each analysis group. As a result, all of the explanatory variables(weekday vs. weekend, metropolitan area vs. non-metropolitan area, male vs. female, commute time vs. other time, routine purpose vs. non-routine purpose) were found to be different across households. In addition, travel distances per trip of weekday, metropolitan area, male, commute time, and non-routine purpose are longer than the opposite variables. Also, the trip distances of small size(1 to 2 persons) households are shorter compared to large size(more than 5 persons) households.

• Journal of Korean Society of Steel Construction
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• v.26 no.5
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• pp.485-498
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• 2014

In this paper, vehicle-bridge interaction analysis under a series of moving vehicles to simulate a lane load was performed to estimate impact factor of the main cable, hanger and girder for the selected suspension bridges with 404m and 1545m main span. Korea Bridge Design Code(Limit State Design) was selected for the live model in which KL-510 truck was modeled 6-d.o.f. vehicle and a lane load was simulated by a series of single-axle vehicles. For the 404m main span bridge, hinge-type and floating-type girders at the tower were considered to examine the impact factor according to the connection and supporting type of the girders. The parameters considered herein are the types of live load-a truck only and a truck plus lane load, eccentricity of moving vehicles, road surface roughness and vehicle speed. The road surface roughness was randomly generated based on ISO 8608 and it was applied to the truck only. The impact factors were also evaluated by using the influence line method that is commonly used in cable-supported bridges and compared with those from vehicle-bridge interaction analysis.

• Korean Journal of Food Science and Technology
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• v.45 no.1
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• pp.104-110
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• 2013

This study assessed hazards at the harvest stage of strawberry farms which may cause risk to humans. A total of 216 samples were collected from 6 strawberry farms (soil culture farms: A, B, C; nutriculture farms: D, E, F) located in Western Gyeongnam. The collected samples were subjected for sanitary indicator bacteria (aerobic plate count, coliforms and Escherichia coli), major foodborne pathogens (E. coli O157:H7, Listeria monocytogenes, Salmonella spp., Staphylococcus aureus and Bacillus cereus), and fungi. The levels of APC and coliform in the soil culture farms were 1.0-6.9 and 0.4-4.6 log CFU/g (leaf, mL, hand or 100 $cm^2$), respectively. The samples obtained from the nutriculture farms were contaminated with the levels of 0.8-4.9, and 0.2-2.6 log CFU/g (leaf, mL, hand or 100 $cm^2$) of APC and coliform. However, E. coli was not detected in any samples. In major foodborne pathogens, S. aureus was detected at the level of ${\leq}$3.3 log CFU/hand in workers' hand samples and B. cereus was detected at the levels of 0.4-4.1 log CFU/g (hand or 100 $cm^2$) in soil, plants and workers' hygiene. L. monocytogenes, E. coli O157:H7 and Salmonella spp. were not detected. Fungi were detected at the levels of 1.0-5.2 and 0.2-4.4 log CFU/g (leaf, mL, hand or 100 $cm^2$) in soil culture and nutriculture farms, respectively.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.