Exploring Travel Mode Preference of External Trips for Smart City Transportation Planning: Sejong, Korea

Jin Ki Eom · Kwang-Sub Lee · Sangpil Ko · Jun Lee

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Transportation Planning and Optimization Urban Transport and Accessibility Transportation and Mobility Innovations

Abstract

In the face of growing concerns about urban problems, smart cities have emerged as a promising solution to address the challenges, for future sustainable societies in cities. Since the early 2000s, 67 local governments in Korea have been participating in smart city projects, as of 2019. The Sejong 5-1 Living Area smart city was selected as one of two pilot national demonstration smart cities. The main objectives of this study are to introduce the Sejong 5-1 Living Area smart city project that is currently in the planning stage, present travel and mode preferences focusing on external trips in a smart city context to be built, and analyze a mode choice model according to the socioeconomic characteristics of individual travelers. One of the distinguishing features of the Sejong smart city is its transportation design concept of designating a sharing car-only district within the city to limit private vehicle ownership to about one-third of residents, while bus rapid transit (BRT) plays a central role in mobility for external trips among four transport modes including private cars, BRT, carsharing, and ridesharing. This study was analyzed using the stated preference survey data under hypothetical conditions by reflecting the unique characteristics of the Sejong smart city transportation policy. Approximately two-thirds of respondents in the survey preferred to spend less than 1.25 USD, traveling less than 35 min on BRT trips. On the basis of the survey data, we developed a mixed logit mode choice model and found the overall model estimates to be statistically significant and reasonable. All people-specific variables examined in this study were associated with mode choices for external commuting trips, including age, income, household size, major mode, driving ability, and presence of preschoolers.

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References

1972Conditional logit analysis of qualitative choice behavior 2000Stated Choice Methods 2003The Mixed Logit model: The state of practice 2020Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models 2005Accounting for heterogeneity in the variance of unobserved effects in mixed logit models 2009Latent class model or mixed logit model? A comparison by transport mode choice data 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model 2016Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands

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