Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice

Eldeeb, Gamal; Mohamed, Moataz; Páez, Antonio

doi:10.1016/j.jtrangeo.2021.103158

Cited by 30 publications

(21 citation statements)

References 69 publications

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“…Another interesting phenomenon was that with the increase in the distance from CBD, public transport and walking also seemed to show obvious competitive changes. This finding is inconsistent with most existing studies (e.g., (Chen et al [ 54 ]; Eldeeb et al [ 55 ])), but it is reasonable. First, unlike the highly concentrated employment distribution in western countries such as the United States, Guiyang, as a typical mountainous city in Western China, may have more dispersed employment and business distribution.…”

Section: Resultscontrasting

confidence: 94%

Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

Shi

et al. 2022

IJERPH

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Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.

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Section: Resultscontrasting

confidence: 94%

Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

Shi

et al. 2022

IJERPH

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“…Te built-environment characteristics that relate to residents' travel behavior are defned from "3Ds" into "5Ds," which are "density," "diversity," block "design," "destination," "accessibility," and "distance" to transit [15,16]. Some scholars specifcally focused on the impact of the "5Ds," as refection of built-environment characteristics, on active travel (e.g., see [7][8][9][10][11][12][13][14][17][18][19]).…”

Section: Association Between the Built Environment And Activementioning

confidence: 99%

“…Many studies on travel behavior have found that the built environment and sociodemographics are strongly associated with walking and cycling (e.g., see [7][8][9][10][11][12][13][14]). Individuals' active travel choices are infuenced by built-environment characteristics such as walk-bike infrastructure [7], street pattern [8], route connectivity [9], street greenery [10], and population density [11].…”

Section: Introductionmentioning

confidence: 99%

“…Changes in the built environment, according to Handy et al [12], infuence travel mode choice primarily by altering travel time. Furthermore, Eldeeb et al discovered that improving the built environment does not have a homogeneous impact on the likelihood of using active mode in diferent parts of the city [13]. However, rare studies have focused on the commuting time of active mode and examined whether the built environment has a spatially diferent impact on it.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Heterogeneity in the Nonlinear Impact of Built Environment on Commuting Time of Active Users: A Gradient Boosting Regression Tree Approach

Tang

Shen

et al. 2023

Journal of Advanced Transportation

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Many studies provided evidence regarding the influence of built environment (BE) on commuting time. However, few studies have considered the spatial heterogeneity of such impacts. Using data from Nanjing, China, this study employs two-step clustering and gradient boosted regression trees (GBRT) to segment the neighborhoods into different types and investigate the effects of BE characteristics on the commuting time of active users. The results show a strong effect of BE characteristics on commuting time, involving active modes. The importance of BE characteristics varies among neighborhood types. For active commuters in the internal region of Nanjing, commuting time is affected mostly by the land use mix at the work end. The lowest impact of BE in internal regions is associated with metro station density. For active commuters in external region of the city, the relative importance of intersection density at the home end is the largest (as high as 5.76%). Moreover, other significant differences are found in the associations between BE characteristics and active commuting time in the two regions.

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“…With the support of the above views, numerous studies have linked the built environment with travel behavior and travel CO 2 emissions. Some studies have suggested that built environment factors such as land use diversity and density affect travel distance and travel mode choice, and travel distance and travel mode are closely related to transportation CO 2 emissions [37][38][39][40][41][42]. Ding et al examined the effects of the built environment on travel distance and energy consumption and found significant differences between commuting and non-commuting trips [24].…”

Section: Introductionmentioning

confidence: 99%

How Does Perceived Neighborhood Environment Affect Commuting Mode Choice and Commuting CO2 Emissions? An Empirical Study of Nanjing, China

Chen

Zhou

Huang

2022

IJERPH

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Exploring the impacts of perceived neighborhood environment on commuting behavior and travel-related CO2 emissions helps policymakers formulate regional low-carbon transport policies. Most studies have examined the impact of the objective measures of built environment on travel behavior and related CO2 emissions, and few studies have focused on perceived neighborhood environment. This study develops a structural equation model and uses data from a self-administered survey of urban full-time employees in Nanjing, China to examine the direct and indirect effects of perceived neighborhood environment on commuting mode choice and commuting CO2 emissions. The study shows that perceived service facilities has a significant direct effect on commuting mode and a significant indirect effect on commuting CO2 through the mediating effect of commuting mode choice. While socio-demographic variables such as gender have a significant direct impact on commuting mode and commuting CO2 emissions, they have an indirect impact on commuting mode and commuting CO2 emissions through the intermediate variables (such as car ownership, perceived neighborhood environment and commuting distance). The conclusions of this study show that the potential of commuting CO2 emissions reduction in China is enormous, and that policy interventions on commuting would help developing countries such as China achieve the goals of low-carbon transport and sustainable development.

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Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice

Cited by 30 publications

References 69 publications

Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

Spatial Heterogeneity in the Nonlinear Impact of Built Environment on Commuting Time of Active Users: A Gradient Boosting Regression Tree Approach

How Does Perceived Neighborhood Environment Affect Commuting Mode Choice and Commuting CO2 Emissions? An Empirical Study of Nanjing, China

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