• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.117-127
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• 2009

This study analyzed the uncertainty of the forecasted link traffic flow, and estimated of the interval link flow using Korea Transport Data Base (KTDB) to consider those risks into the feasibility study. In the paper, the uncertainty was analyzed according to the stochastic variation of the KTDB origin-destination traffic. It was found that the uncertainty of the entire network traffic forecasts was 15.4% in average,. when the stochastic variation of the KTDB was considered. The results showed that the more congested the roads were, the bigger the uncertainty of forecasted link traffic flow were found. In particular, we estimated the variance of the forecasted traffic flow, and suggested interval estimates of the forecasted traffic flow instead of point estimates which were presented in the common feasibility studies. These results are expected to contribute the quantitative evaluation of uncertain road investment projects and to provide valuable information to the decision makers for the transport investment.

• Journal of Korean Society of Transportation
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• v.33 no.2
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• pp.113-124
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• 2015

The KOTI(Korea Transport Institute) released the new version of KTDB(Korea Transport DataBase) in public. The new KTDB is different from the past KTDB in using the concept of trip generation and trip attraction instead of using the concept of Origin-Destination (OD), which was used in the past KTDB. Thus, the appropriate analysis method for future travel demand became necessary for the new type of KTDB. The method should be based on the concept of PA(Production-Attraction). This study focused on analysis of trip generation and trip distribution related to newly generated trips by future land developments. The study also described clearly the standardized forecasting process and methods with PA travel tables. The study showed that the analysis results with OD-based analysis can be different from the results with PA-based analysis in forecasting travel demand for a simple example case even though they used exactly same orignal travel data. Therefore, this study emphasized that a proper method should be applied with the new PA-based KTDB. It is necessary to prepare and disseminate guidelines of the proper forecasting method and application with PA-based travel data for practician.

• Journal of the Korean Regional Science Association
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• v.40 no.2
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• pp.57-73
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• 2024

The purpose of this study is to establish the central hierarchy and urban spatial structure of Daejeon in 2040 using differentiated analysis indicators. In order to establish a central hierarchy in the future urban space of Daejeon City, three indicators were derived: population density, use of buildings (commercial, office, residential, etc.) and traffic volume data(KTDB). The results of analyzing the center hierarchy using indicators are as follows. First, the primary centers responsible for urban (core) functions are set in three areas, including Dunsan downtown, the original downtown (Daejeon Station area, designated as a special urban convergence zone), and Doan New Town, and the secondary centers corresponding to sub-city centers (regional bases). was set at 9, 10 tertiary centers corresponding to regional centers (living area centers), and a center hierarchy was established with 5 new specialized bases. Second, new secondary and tertiary centers emerged that were different from the center hierarchy in the 2030 Daejeon Urban Basic Plan. The three indicators used in this study yielded meaningful results in establishing urban spatial structure and central hierarchy that can secure urban competitiveness.

• Journal of Environmental Policy
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• v.14 no.3
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• pp.3-19
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• 2015

In this paper, we reviewed the method of replacing the number of registered vehicles with the number of trips to more realistically calculate vehicle emissions. Using the Korea Transport Data Base (KTDB) in replacing the number of registered vehicles with adjusted number of registered vehicle by specific vehicle type in the metropolitan area, the results by region showed that Seoul had the widest rate of error and that, among vehicles, trucks had the widest rate of error. Also, the absolute value of deviation of registered vehicles and adjusted number of registered vehicle influenced by the calculation of the quantity of vehicle emissions showed that out of the metropolitan regional government all trucks showed the widest deviation. The results of calculating the quantity of vehicle emissions showed an average of 9% difference between the emissions based on the number of registered vehicles and the emissions based on adjusted number of registered vehicle.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.47-57
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• 2016

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS : This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City's O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.