• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.68-79
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• 2014

This paper analyzes the characteristics of trip chaining activities of elderly and explores temporal and spatial distribution. The research also estimates ordered probit model and binary logistic model to investigate various factors affecting trip chaining and mode choice patterns. We utilized household survey data for elderly conducted in 2006 and 2010 in Seoul metropolitan area. Research results indicate that trip chaining showed an increasing trend and simple trip chaining counts for more than 85%. GIS mapping expressed spatial distribution of trip departure and arrival areas, particularly showing regional changes in job-related trips. We also found that more factors influence trip chaining in 2010, compared with 2006, and travel cost is more sensitive than travel time in determining travel mode. The research contributes to establish transportation policies based on travel behavior of elderly in a upcoming super-aged society.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.679-686
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• 2012

KTX step 1 April 2004, after the opening, the second phase of the project was opened in November 2010. High-speed rail after the opening and continue to increase the demand of high-speed rail, Have the speed of competitive advantage compared too the means of transportation. The opening of these high-speed rail has led to changes of the move, the company's position, and the spatial structure of the population of reorganization, such as the social, economic, transportation. In this study, survey data using the High Speed Rail Station EMME/2 of the program to take advantage of the 2-Dimentional Blancing trip distribution to investigate the passage through the trip distribution by the estimation of the parameters of the model to estimate the distribution of the means of access and high-speed rail station to reproduce and Analysis of the results by means of access parameters (${\theta}$) autos 0.0395, buses 0.0390, subway 0.0650, taxi 0.0415, the frequency distribution (Trip Length Frequency Distribution: TLFD) were analyzed survey data value model with the results of comparing $R^2$ cars analysis and model values similar survey data 0.909 bus 0.923, subway 0.745 to 0.922, taxi, F test P value analysis is smaller than 0.05 at the 95% confidence level as a note that was judged to have been. Trip frequency distribution analysis, but in the future, set the unit to 5km-trip frequency distribution middle zone Units from small zone units (administrative district) segmentation research is needed, and can reflect the trip distance 0~5 km interval combined function to take advantage of the gravity model and the 3-Dimentional Blancing applied research is needed to be considered.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.51-61
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• 2023

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.143-150
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• 2005

Estimation of trip distribution, estimated O-D matrix must satisfy the condition that the sum of trips in a row should equal the trip production, and the sum of trips in a column should equal the trip attraction. In most cases the iterative calculation for convergence is needed to satisfy this condition. Most of all present convergence of iterative methods may results a big difference between estimated value and converged value, and from this, the trip distribution patterns may be changed. This paper presents a new convergence of iterative method that comes closer to meeting the convergence condition and gives the maximum likelihood estimation for calculating a distribution patterns from the trip distribution estimation model. The newly developed method differs from existing methods in three important ways. First, it simultaneously considers both the convergence condition and the distribution patterns. Second, it computers simultaneous convergence of rows and columns instead of iterating respectively. Third, instead of using the growth rates to the trip production, trip attraction, it uses the differences between trip production and sum of trips in a row, and trip attraction and sum of trips in a column. Using 38 by 38 O-D matrix, this paper compared the Fratar method and the Furness method to the newly developed method and found that this method was superior to the other two methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.599-617
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• 2006

A network model and a Genetic Algorithm(GA) is proposed to solve the simultaneous estimation of the trip distribution and traffic assignment from traffic counts in the congested networks in a logit-based Stochastic User Equilibrium (SUE). The model is formulated as a problem of minimizing the non-linear objective functions with the linear constraints. In the model, the flow-conservation constraints of the network are utilized to restrict the solution space and to force the link flows meet the traffic counts. The objective of the model is to minimize the discrepancies between the link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links and the link flows estimated through the traffic assignment using the path flow estimator in the legit-based SUE. In the proposed GA, a chromosome is defined as a vector representing a set of Origin-Destination Matrix (ODM), link flows and travel-cost coefficient. Each chromosome is evaluated from the corresponding discrepancy, and the population of the chromosome is evolved by the concurrent simplex crossover and random mutation. To maintain the feasibility of solutions, a bounded vector shipment is applied during the crossover and mutation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.417-424
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• 2006

To get more accurate trip distribution estimation results, this study developed an improved gravity model. Using three different year's O-D table resulted from person trip survey, this study analyzed correlative between basic year's residuals and target year's residuals by gravity model. And resulted that the two have linear correlation. From this, improved gravity model was developed as adding basic year's residual to present gravity model. Developed gravity model was compared to present gravity model by estimation accuracy, and revealed that distributed trips from improved gravity model was more closer to real O-D than distributed trips from present gravity model.

• Journal of Korean Society of Transportation
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• v.31 no.2
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• pp.60-68
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• 2013

This study developed plural gravity models and their application method in order to increase the accuracy of trip distribution estimation. The developed method initially involves utilizing the coefficient of determination ($R^2$) to set the target level. Afterwards, the gravity model is created, and if the gravity model's coefficient of determination is satisfactory in regards to the target level, the model creation is complete and future trip distribution estimation is calculated. If the coefficient of determination is not on par with the target level, the zone pair with the largest standardized residual is removed from the model until the target level is obtained. In respect to the model, the removed zone pairs are divided into positive(+) and negative(-) sides. In each of these sides, gravity models are made until the target level is reached. If there are no more zone pairs to remove, the model making process concludes, and future trip distribution estimation is calculated. The newly developed plural gravity model and application method was adopted for 42 zone pairs as a case study. The existing method of utilizing only one gravity model exhibited a coefficient of determination value ($R^2$) of 51.3%, however, the newly developed method produced three gravity models, and exhibited a coefficient of determination value ($R^2$) of over 90%. Also, the accuracy of the future trip distribution estimation was found to be higher than the existing method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.763-769
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• 2006

Gravity Model estimates target year's distributed trips using three variables like as origin zone's trip production, destination zone's trip attraction and traffic impedance between origin zone centroid and destination zone centroid. Estimating inner zone trip by gravity model is impossible because traffic impedance of inner zone has "0" value. So till today, for estimating inner zone trips, other methods like growth factor model are used. This study proposed inner zone trip estimation method that calculates inner zone's traffic impedance using established gravity model and estimates inner zone trips by putting calculated traffic impedance into the gravity model. 1988 year's surveyed O-D as basic year's O-D, proposed method's and existing methods(growth factor method and regression model)'s estimated results of 1992 year's and 2004 year's were compared with each year's real O-D by $x^2$, RMSE, Correlation coefficient. And resulted that the proposed method is superior than other existing methods.

• Journal of Korean Society of Transportation
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• v.33 no.6
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• pp.575-583
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• 2015

The Korea Transport Institute (KOTI) builds the origin and destination(O-D) trip data with relatively smaller zone size such as Eup, Myeon, Dong administration unit districts in metropolitan area. Otherwise, O-D trip data was built by bigger size of traffic analysis zone(TAZ) such as Si, Gun, Gu administration unit districts for rural area. In some cases, it is needed to divide a zone into several sub-zones for rural area in order to analyze travel distribution pattern in detail for a certain highway and rail project. The study suggested a method to estimate O-D trips for sub-zones in the larger-size zones in rural area. Two different distribution models, direct demand model and gravity model, were calibrated for sub-zone's intra-zonal O-D trip pattern with metropolitan area O-D data which has smaller zone-size (sub-zone) data categorized by low, middle and high population density. The calibration results were compared between the two models. The gravity model with impedance function of power functional form was selected with better explanation for all groups in the metropolitan area. The adjusted $R^2$ was 0.7426, 0.6456 and 0.7194 for low, middle and high population density group, respectively. The suggested O-D trip estimating method is expected to produce enhanced trip patterns with sub-divided small zones.

• Journal of Korean Society of Transportation
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• v.7 no.2
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• pp.45-52
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• 1989

The network equilibrium theory is to estimate the travel choices on a transportation network when the resulting travel times and costs are one basis for the choices. Increasing use of this principle on travel assignment problem lead to develop the combined choice models including not only travel options such as mode and route, but location options like trip distribution problems. This paper, first, reviews earlier developments of variable demand network equilibrium models, combined modeles of trip distribution and assignment, and entropy constrained combined models. Then various model structures of combining travel choice models based on network equilibrium theory and entropy constraints are discussed.