Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups

Ha, Jaehyun; Lee, Sugie; Ko, Joonho

doi:10.1016/j.tra.2020.07.020

Cited by 55 publications

(29 citation statements)

References 58 publications

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“…To promote the use of public transport, new bus stops and routes should be added to transport systems, and the existing infrastructure should be upgraded. This finding was broadly in agreement with the study carried out by Ha et al (2020) , which found that travel time significantly influences mode choice, and also suggested that policies should be elaborated with a view to creating new infrastructure, in order to facilitate a modal shift away from driving to public transit. In addition, more public transport modes, including underground and light rail, should be introduced.…”

Section: Findings and Discussionsupporting

confidence: 90%

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Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study

Yang

Cao

Cheng

et al. 2021

Transportation Research Interdisciplinary Perspectives

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During the COVID-19 crisis, a series of measures were taken to restrict travel and social activities outside the home in order to curb the pandemic and ameliorate its negative effects. These unprecedented measures have had a profound impact on the number and purposes of trips and modes of travel. In China, although the pandemic is now generally under control and transport availability has returned to nearly normal, the extent of the changes in travel behaviour wrought during and after the pandemic still remains unclear. Therefore, the aim of this paper is to investigate the differences in individual travel behaviours during and after the COVID-19 pandemic, using Huzhou as an example. Semi-structured interviews were used to examine the influence of COVID-19 on the travel behaviour and perceptions of different groups. The results indicate that, initially, travel demand was greatly reduced. Second, decreased travel reduced participation in activities, which can have adverse effects on people’s health as well as their subjective well-being. Third, the degree and duration of such impacts varied from person to person. Students, lower income cohorts, groups living in small communities with insufficient green spaces, and those working in tourism, catering, informal businesses and transport-related sectors were more vulnerable than others. Policymakers, urban and transport planners should therefore pay attention to the social inequities that arise from unequal access to transport and heterogeneity between individuals. Additionally, public transport systems require further development to promote social cohesion.

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Section: Findings and Discussionsupporting

confidence: 90%

“…Research has shown that people of different income levels and age groups react instinctively ( Ha et al, 2020 ). Although older adults were more likely to be infected by COVID-19, this does not mean that they were in a more difficult situation.…”

Section: Findings and Discussionmentioning

confidence: 99%

Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study

Yang

Cao

Cheng

et al. 2021

Transportation Research Interdisciplinary Perspectives

View full text Add to dashboard Cite

show abstract

“…While the relationship between environmental worldviews and pro-environmental behavior remain uncertain [74], these results suggest greater environmental consciousness may be associated with more sustainable transportation behavior. Age was the only demographic differentiating factor, as those born after 1988 were classified as multimodal and those born before 1988 were classified as non-vehicle, aligning with previous research [75][76][77][78]. Younger individuals may be more flexible in their adaptation to multimodal transportation options while older individuals may experience reduced access to private automobile ownership and use.…”

Section: Discussionsupporting

confidence: 78%

Factors associated with emerging multimodal transportation behavior in the San Francisco Bay Area

Wells¹,

Small²,

Spurlock³

et al. 2021

Environ. Res.: Infrastruct. Sustain.

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This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.

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“…Regarding car, age is the most important variable in determining choice, which may indicate the varying preference for comfort and value of time by age [34]. Car ownership, of course, is the second important variable for the choice of a car.…”

Section: Variable Importancementioning

confidence: 99%

Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach

Kim

2021

Journal of Advanced Transportation

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Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.

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Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups

Cited by 55 publications

References 58 publications

Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study

Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study

Factors associated with emerging multimodal transportation behavior in the San Francisco Bay Area

Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach

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