Special Issue "New Perspectives on Transportation Mode Choice Decisions"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editor

Prof. Joonho Ko
E-Mail Website
Guest Editor
Graduate School of Urban Studies, Hanyang University, Seoul 04763, Korea
Interests: smart mobility; sustainable transportation planning; travel behavior

Special Issue Information

Dear Colleagues,

Mode choice is one of the most vital stages in transportation planning processes, as it directly affects policy-making decisions. Therefore, the sound understanding of what factors are crucial for mode choices would be the first step toward the right policy decisions. The encouragement of sustainable transportation mode uses should also be designed based on such an understanding. It is well known that a variety of factors influence travelers’ mode choice decisions. However, we are still not sure about under what conditions the factors are more (or less) important. This is because cities have different contexts such as physical environment, governmental policies, and culture.

In recent days, we have observed the emergence of new mobility options, including autonomous vehicles, shared transport, and personal mobility. Answering how people will respond to the new mobility options (e.g., acceptance of the mode) has become an important research issue. A flux of big data created by our everyday life is expected to facilitate research activity.

This Special Issue intends to widen our knowledge on mode choice behavior by inviting papers on (but not limited to):

  • Mode choice models or new approaches encompassing a wide range of factors including travelers’ psychological aspects;
  • Demand estimation of new mobility options, including urban air mobility and autonomous vehicles;
  • Travel behavior related to sustainable transportation mode uses;
  • Case studies that evaluate the impacts of transport policies on mode choice;
  • Value of time and mode choice decisions;
  • Big data applications in explaining mode choice behavior.

Guest Editor
Prof. Joonho Ko

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  Factors associated with mode choice decisions
  •  Smart mobility and its demand
  •  Travel behavior related to modal shift
  •  New mobility options
  •  Value of time and mode choice

Published Papers (5 papers)

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Research

Article
Consumers’ Intention to Purchase Electric Vehicles: Influences of User Attitude and Perception
Sustainability 2021, 13(12), 6778; https://doi.org/10.3390/su13126778 - 15 Jun 2021
Cited by 1 | Viewed by 512
Abstract
Electric vehicles (EVs) have been developed as an efficient solution to reduce automobile emissions. To ensure the effective diffusion of EVs in current transport systems, it is vital to understand the factors affecting consumers’ intentions to purchase EVs. To provide insights for this [...] Read more.
Electric vehicles (EVs) have been developed as an efficient solution to reduce automobile emissions. To ensure the effective diffusion of EVs in current transport systems, it is vital to understand the factors affecting consumers’ intentions to purchase EVs. To provide insights for this understanding, this study aims to investigate such factors with a particular focus on users’ attitudes and perceptions. A questionnaire survey was conducted in September 2019 among potential consumers in the major cities of South Korea. A total of 1500 valid survey responses were obtained, and investigations using binary logistic regression and regression tree were conducted for an empirical analysis. The results showed that among attitudinal attributes, environmental and economic perceptions concerning EV use were the strongest predictors for an EV purchase. In addition, technological concerns were found to have negative impacts on EV purchase intentions. The findings of this study could provide reasonable guidelines for establishing marketing strategies and serve as a reference for EV stakeholders to improve the applicability of current policies regarding EV adoption. Full article
(This article belongs to the Special Issue New Perspectives on Transportation Mode Choice Decisions)
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Article
Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model
Sustainability 2021, 13(7), 3780; https://doi.org/10.3390/su13073780 - 29 Mar 2021
Viewed by 554
Abstract
With the increasing level of air pollution and fine dust, many countries are trying to prevent further environmental damage, with various government legislations, such as the Kyoto Protocol and the Paris Agreement. In the transportation field, a variety of environmental protection schemes are [...] Read more.
With the increasing level of air pollution and fine dust, many countries are trying to prevent further environmental damage, with various government legislations, such as the Kyoto Protocol and the Paris Agreement. In the transportation field, a variety of environmental protection schemes are also being considered (e.g., banning old diesel vehicles, alternate no-driving systems, electric car subsidies, and environmental cost charging by tax). Imposing environmental constraints is a good approach to reflect various environmental protections. The objective of this research was to analyze the mode-choice and route-choice changes based on imposing environmental constraints. For the objective, a combined modal split and traffic assignment (CMA) model with an environmental constraint model was developed. For the environmental constraint, carbon monoxide (CO) was adopted, because most of the CO emissions in the air are emitted by motorized vehicles. After a detailed description of the model, the validity and some properties of the model and algorithm are demonstrated with two numerical examples (e.g., a small and a real network in the city of Winnipeg, Canada). From the numerical results, we can observe that imposing the small restriction (or strict) value has more efficiency in mode change and reducing network emission. Full article
(This article belongs to the Special Issue New Perspectives on Transportation Mode Choice Decisions)
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Article
Sustainable Mobility Policy Analysis Using Hybrid Choice Models: Is It the Right Choice?
Sustainability 2021, 13(5), 2993; https://doi.org/10.3390/su13052993 - 09 Mar 2021
Viewed by 681
Abstract
In recent years, sustainable mobility policy analysis has used Hybrid Choice Models (HCM) by incorporating latent variables in the mode choice models. However, the impact on policy analysis outcomes has not yet been determined with certainty. This paper aims to measure the effect [...] Read more.
In recent years, sustainable mobility policy analysis has used Hybrid Choice Models (HCM) by incorporating latent variables in the mode choice models. However, the impact on policy analysis outcomes has not yet been determined with certainty. This paper aims to measure the effect of HCM on sustainable mobility policy analysis compared to traditional models without latent variables. To this end, we performed mode choice research in the city of Santander, Spain. We identified two latent variables—Safety and Comfort—and incorporated them as explanatory variables in the HCM. Later, we conducted a sensitivity study for sustainable mobility policy analysis by simulating different policy scenarios. We found that the HCM amplified the impact of sustainable mobility policies on the modal shares, and provided an excessive reaction in the individuals’ travel behavior. Thus, the HCM overrated the impact of sustainable mobility policies on the modal switch. Likewise, for all of the mode choice models, policies that promoted public transportation were more effective in increasing bus modal shares than those that penalized private vehicles. In short, we concluded that sustainable mobility policy analysis should use HCM prudently, and should not set them as the best models beforehand. Full article
(This article belongs to the Special Issue New Perspectives on Transportation Mode Choice Decisions)
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Article
Relationship between Land Use Mix and Walking Choice in High-Density Cities: A Review of Walking in Seoul, South Korea
Sustainability 2021, 13(2), 810; https://doi.org/10.3390/su13020810 - 15 Jan 2021
Cited by 1 | Viewed by 613
Abstract
This study confirmed the general belief of urban planners that mixed land use promotes walking in Seoul, a metropolis in East Asia, by analyzing the effect of mixed land use on the travel mode choice of housewives and unemployed people who make non-commuting [...] Read more.
This study confirmed the general belief of urban planners that mixed land use promotes walking in Seoul, a metropolis in East Asia, by analyzing the effect of mixed land use on the travel mode choice of housewives and unemployed people who make non-commuting trips on weekdays. Using binomial logistic regression of commuting data, it was found that the more mixed a neighborhood environment’s uses are, the more the pedestrians prefer to walk rather than drive. The nonlinear relationship between the land use mix index and the choice to walk was also confirmed. Although mixed land use in neighborhoods increased the probability of residents choosing walking over using cars, when the degree of complexity increased above a certain level, the opposite effect was observed. As the density of commercial areas increased, the probability of selecting walking increased. In addition to locational characteristics, income and housing type were also major factors affecting the choice to walk; i.e., when the residents’ neighborhood environment was controlled for higher income and living in an apartment rather than multi-family or single-family housing, they were more likely to choose driving over walking. Full article
(This article belongs to the Special Issue New Perspectives on Transportation Mode Choice Decisions)
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Article
Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior
Sustainability 2020, 12(18), 7481; https://doi.org/10.3390/su12187481 - 11 Sep 2020
Viewed by 564
Abstract
Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. [...] Read more.
Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior. Full article
(This article belongs to the Special Issue New Perspectives on Transportation Mode Choice Decisions)
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