• Journal of the Korean Society for Railway
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• v.12 no.5
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• pp.656-662
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• 2009

In general, the analysis of travelers' mode choice behavior is accomplished by developing the utility functions which reflect individual's preference of mode choice according to their demographic and travel characteristics. In this paper, we propose a methodology that takes the spatial effects of individuals' departure locations into account in the mode choice model. The statistical models considered here are spatial logistic regression model and conditional autoregressive model taking a spatial association parameter into account. We employed the Bayesian approach in order to obtain more reliable parameter estimates. The proposed methodology allows us to estimate mode shares by departure places even though the survey does not cover all areas.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.813-821
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• 2009

In general, the analysis of travelers' mode choice behavior is accomplished by developing the utility functions which reflect individual's preference of mode choice according to their demographic and travel characteristics. In this paper, we propose a methodology that takes the spatial effects of individuals' departure locations into account in the mode choice model. The proposed methodology allows us to estimate mode shares by departure places even though the survey does not cover all areas. This will help transportation agencies to evaluate how the difference of individuals' departure places such as residential, retail, and commercial area affects on mode choice behaviors.

• IE interfaces
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• v.15 no.4
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• pp.474-482
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• 2002

In this paper, we propose a parametric optimization approach to simultaneously determining trip distribution, mode choice, and user-equilibrium assignment. In our model, mode choice decisions are based on a binomial logit model and passenger and cargo demands are divided into appropriate mode according to the user equilibrium minimum travel time. Underlying network consists of road and rail networks combined and mode choice available is auto, bus, truck, passenger rail, and cargo rail. We provide an equivalent convex optimization problem formulation and efficient algorithm for solving this problem. The proposed algorithm was applied to a large scale network examples derived from the National Intermodal Transportation Plan (2000-2019).

• Journal of Korean Society of Transportation
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• v.10 no.3
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• pp.21-42
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• 1992

As for the travel demand analysis of the past, forcasting has been conducted by the use of revealed preference(RP) informations about actual or observed choices made by individuals. Forcasting method using RP data needs implicit assumptions that there will be no remarkable changes in existing transport conditions. However in case of occuring the great changes in existing conditions or adding a new choice-set of hypothetical options, it is very difficult to predict future travel demand. Fortunately in recent years, especially in the mode choice analysis, it has been perceived that the importance of individual performance data using stated preference(SP) experiments as well as RP data. But the research reports has not been reported sufficiently from models estimated using SP data. Under this background, we analyze the factors affecting the mode choice behavior as a fundamental study against the modelling task with SP choice data. For this analysis, we assumed subway operations in the secondary cities where there are no subway lines until now, and set up a choice-set of hypothetical options based on Experimental Design Method.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.81-89
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• 2011

Trip distribution is the second step of the conventional travel demand estimation process, which connects trips between origin and destination, while transport mode choice is the third step of the process, which chooses transport mode among several modes serving for each origin-destination pair. Although these two steps have closely connected, they have been estimated independently each other in the estimation procedure. This paper presents an integrated model combining trip distribution and transport mode choice, and also presents its solution algorithm. The model integrates gravity model adopted for the trip distribution process with logit model employed for the mode choice process. The model would be expected to cope with the inconsistency issue existing in the conventional travel demand estimation procedure. This paper also presents an equilibrium condition, sensitivity of the model, and compares them with those of existing models.

• Journal of the Korean Society for Railway
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• v.19 no.1
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• pp.87-96
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• 2016

The multinomial logit model, based on random utility maximization (RUM) theory, has been the predominant model used in travel mode choice contexts. In this paper, the travel mode choice model based on random regret minimization (RRM) theory is proposed as an alternative to the RUM model, and the applicability of the RRM model is examined. The presented model is applied to the case of inter-city travel mode choice in Korea. The empirical results show that the RUM model and RRM model have parameters that are consistent with the intuition. The goodness of fit statistics in the RRM model improved compared with the results of the RUM model. Consequently, these results show the possibility of using the RRM model in the context of travel mode choice.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.57-66
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• 2003

Because the Logit model easily calculates probabilities for choice alternatives and estimates parameters for explanatory variables, it is widely used as a traffic mode choice model. However, this model includes an assumption which is independently and identically distributed to the error component distribution of the mode choice utility function. This paper is a study on the estimation of the Heteroscedastic Logit Model. which mitigates this assumption. The purpose of this paper is to estimate a Logit model that more accurately reflects the mode choice behavior of passengers by resolving the homoscedasticity of the model choice utility error component. In order to do this, we introduced a scale factor that is directly related to the error component distribution of the model. This scale factor was defined so as to take into account the heteroscedasticity in the difference in travel time between using public transport and driving a car, and was used to estimate the travel time parameter. The results of the Logit Model estimation developed in this study show that Heteroscedastic Logit Models can realistically reflect the mode choice behavior of passengers, even if the difference in travel time between public and private transport remains the same as passenger travel time increases, by identifying the difference in mode choice probability of passengers for public transportation.

• Land and Housing Review
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• v.11 no.2
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• pp.1-14
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• 2020

The aim of this study is to empirically identify the differentiating characteristics of determinant factors on sing-person households' commuting mode choice compared to multi-person households' one in order to establish the customized police directions to decrease private car use in commuting. While the study use the 2% sample survey data on the population and housing in 2015, it employ multinomial logit models on relative choice probability of such alternative commuting modes as bus, subway or rail, and walking, rather than driving. As potential determinant factors, the study employs demographic, socio-economic, and housing and residential one for both models of single-person and multi-person households. The study finds that the behavior of commuting mode choice has distinctive difference by gender, marriage status, physical activity constraint, job type, residential period in current housing of the single-person household's workers compared to the multi-person households' ones. Based on the findings, the study deduce ten commuting policy directions customized for the single-person household.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.183-192
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• 2016

The purpose of this study is to analyze the choice behavior of the mode of transportation for travel from Korea to China. Discrete choice analysis is utilized to establish the factors that affect travelers' choice and to quantify the importance of these factors in transportation mode choices. The proposed choice models were constructed by using stated-preference (SP) data obtained from Chungcheongnam-do. This study also examined different choice behavior in order to capture any previously unobserved differences in the residence area. Results showed that the access time and frequency attributes are the most significant factors, while the travel time attributes are the least significant factors for travelers' choice behaviour. The insights of the results described in this research provide some practical suggestions to transportation providers for planning and strategic management endeavors in the future.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.131-139
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• 2010

The transport mode choice problem is to determine which of the alternative transport modes connecting an origin and destination will be used by a traveler. Most of the research relating to transport mode choice have mainly been focused on modeling, properties, and applications of the model, but rarely were concerned with equilibrium among the modes. This paper proves the equilibrium among the modes by using a logit mode choice model, and then verifies it with the Korean Transport Database (KTDB). In order to obtain such an equilibrium, this paper also presents a solution algorithm based on the fixed point theorem. The algorithm was tested with an example and confirmed the equilibrium solution.