• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.65-72
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• 2000

Travel demand forecasting is the important process of transportation policy and planning, especially trip assignment is also important because it finds deficiency of network GIS can be applied to transportation due to its various merits. Recently Program development environment is changed to component-based and transportation-component is necessary. This study evolves in implementing trip assignment model with GIS and tries to apply the system to the Cheongju City.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1087-1094
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• 2013

This study aims at enhancing the objective estimation of social cost of air quality due to mobile emission. More specifically, it examines the difference between the daily oriented and hourly oriented estimation results of social air quality cost and draws implications from the comparative analysis. The result indicates that the social cost of air quality differs up to approximately 24 times depending on the analysis time period. Moneywise, the difference between daily and hourly assignments amounts to the average of 653.5 billion won whereas only 1% of error occurred in the estimation result based on peak and nonpeak based hourly assignment. This study reaffirms the need for time-based travel demand management for emission reduction, and confirms the feasibility of emission estimation by travel demand forecasting method over the conventional method employed by the CAPSS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.126-139
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• 2011

In the traditional four-step travel demand models, each step is sequentially conducted following the model estimation at the previous step. The accuracy of the following model is partly dependent on whether the model at the former stage was properly established or not. Therefore, trip generation, which is the first step in this conventional model, has great effects on the modeling process and forecasting results. Linear regression models for trip generation of Seoul Metropolitan Area might increase the forcasting errors, since a variety of land-use characteristics are not considered. Hence, in this study, zonal factors such as socioeconomic and land use variables are included to improve the elaboration of trip generation. Comparing the %RMSE with the existing models, which contain bigger errors in the zones highly based on the secondary and tertiary industries than residence-based, the trip generation models including those variables seem more appropriate overall.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.725-732
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• 2016

This study analyzes the number of Jeju-bound tourists according to travellers' purposes. We classify the travellers' purposes into three categories: "Rest and Sightseeing", "Leisure and Sport", and "Conference and Business". To see an impact of MERS outbreak occurred in May 2015 on the number of tourists, we fit seasonal ARIMA-Intervention model to the monthly arrivals data from January 2005 to March 2016. The estimation results show that the number of tourists for "Leisure and Sport" and "Conference and Business" were significantly affected by MERS outbreak whereas arrivals for "Rest and Sightseeing" were little influenced. Using the fitted models, we predict the number of Jeju-bound tourists.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1799-1810
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• 2011

Origin-Destination(OD) trip survey being used in travel demand forecasting has been obtained through totalizing process with direct sample survey techniques such as plate license survey, roadside interview, household travel survey, and cordon line counts. However, the OD survey has many discrepancies in sampling, totalizing process, and such discrepancies contains problems of difference between forecasted traffic volume and observed data. On the other hand, transit smart card data recently collected has credible resource of obtaining travel information for bus and metro. This paper presents parameter estimation of gravity model by using transit smart card data. Through the parameter estimation method, we estimated =0.57, ${\beta}$=0.14 of gravity model for bus, and ${\alpha}$=-0.21, ${\beta}$=0.05 for metro. The statistical test such as T-test, coefficient of correlation, Theil`s inequality coefficient showed no difference between observed volume and estimated volume. Elasticities of bus and metro derived in this paper are also reasonable.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.139-145
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• 2005

`Influence Area` means the area that the traffic flow pattern of which is changed remarkably after the construction of highway facilities. The Influence area would be a significant criteria for demand forecasting and economic analysis. However existing methods for defining influence area such as O/D method, traffic volume variation method and rate of traffic volume variation method have no standard criteria. In this paper, some problems which existing methods have are analyzed and a new method travel time method - is introduced. Influence area can be induced as the area where the vehicles reach from starting traffic zone to ending zone to which 95 percentile vehicles want to travel. In addition, the value of standard criteria for defining influence ayes are induced via this method.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.129-134
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• 2007

In general, based on traffic data in a ideal traffic condition, BPR cost function is used to a variety of transportation policies. However, Some researchers have reported that BRP cost function is not appropriate for analyzing traffic pattern as well as forecasting future demand.(Spiess, 1989 ; Singh, 1999) Therefore, in this paper to solve this problem, a methodology based on data through Micro traffic Simulator Based(MSB) is developed. As a result following outputs are obtained ; (1) presenting a methodology to develop a travel cost function through VISSIM in order to assess transportation policies and (2) developing BRP cost function and MSB cost function from data analysis through VISSIM and verifying availability of MSB function by comparative analysis.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.7-16
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• 2004

The homogeneous road section is defined as one consisted of similar traffic characteristics focused on demand and supply. The criteria, in the aspect of demand, are the diverging rate and the ratio of green time to cycle time at signalized intersection, and distance between the signalized intersections. The criteria, in that or supply, are the traffic patterns such as traffic volume and its speed. In this study, the effective method to generate valuable data, pointing out the problems of removal method of obscure data, is proposed using data collected from Gonjiam IC to Jangji IC on the national highway No.3. Travel times are collected with licence matching method and traffic volume and speed are collected from detectors. Futhermore, the method of selecting homogeneous road section is proposed considering demand and supply aspect simultaneously. This method using outlier filtering algorithm can be applied to generate the travel time forecasting model and to revise the obscured of missing data transmitting from detectors. The point and link data collected at the same time on the rational highway can be used as a basis predicting the travel time and revising the obscured data in the future.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.265-274
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• 2017

For accurate demand forecasting of railway logistics, we estimated intercity freight mode choice models based on the binary logit model and using production-consumption data from the Korea Transport Database. We estimated two types of models and compared the results by major item of railway logistics, such as container, cement, and steel: 1) The aggregate freight mode choice models are based on the revealed preference (RP) data and 2) The disaggregate models are based on the stated preference (SP) data. With respect to the container, the travel time variable was found to be statistically significant; however, the travel cost variable was not statistically significant in the RP model, while the travel cost variable was statistically significant in the SP model. For cement and steel, the travel cost variables were statistically significant but the travel time variables were not statistically significant in either the RP or the SP models. These results are inconsistent with results from previous studies based on SP data, which showed that the travel time variables were significant. Consequently, it can be concluded that the travel time factor should be considered in container transport, but that this factor is negligible for cement and steel transport.

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.93-105
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• 2014

This study evaluated the relative efficiency of mobile emission reduction countermeasures through a Data Envelopment Analysis (DEA) approach and determined the priority of countermeasures based on the efficiency. Ten countermeasures currently applied for reducing greenhouse gases and air pollution materials were selected to make a scenario for evaluation. The reduction volumes of four air pollution materials(CO, HC, NOX, PM) and three greenhouse gases($CO_2$, $CH_4$, $N_2O$) for the year 2027, which is the last target year, were calculated by utilizing both a travel demand forecasting model and variable composite emission factors with respect to future travel patterns. To estimate the relative effectiveness of reduction countermeasures, this study performed a super-efficiency analysis among the Data Envelopment Analysis models. It was found that expanding the participation in self car-free day program was the most superior reduction measurement with 1.879 efficiency points, followed by expansion of exclusive bus lanes and promotion of CNG hybrid bus diffusion. The results of this study do not represent the absolute data for prioritizing reduction countermeasures for mobile greenhouse gases and air pollution materials. However, in terms of presenting the direction for establishing reduction countermeasures, this study may contribute to policy selection for mobile emission reduction measures and the establishment of systematic mid- and long-term reduction measures.