Demographic factors, workplace factors and active transportation use in the USA: a secondary analysis of 2009 NHTS data

Quinn, Tyler D.; Jakicic, John M.; Fertman, Carl I.; Gibbs, Bethany Barone

doi:10.1136/jech-2016-207820

Cited by 18 publications

(32 citation statements)

References 23 publications

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“…The likelihood of active travel rises with population density, on average 30.6% for urban and 23.2% for rural context (in line with Pucher and Buehler, 2006;Quinn et al, 2016). A similar decrease is noted for public transport, an average of 2.7% (table 3).…”

Section: Urban-rural Typology and Household Sizesupporting

confidence: 66%

“…The percentage change decreases with rising distance non-linearly (figure 3), where an increase from 5 to 10 km per return trip reduces active travel by 6.8%, from 10 to 15 km by only 5.9%, and so on. Thus, lowering distances widens the travel mode choice (see also Chapman et al, 2016;Pucher and Buehler, 2006;Quinn et al, 2016). There is a slight increase in the likelihood of opting for public transport (0.5%) with one-km distance rise, though public travel is less susceptible to changing distance (table 3).…”

Section: Mobilitymentioning

confidence: 99%

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Carbon mitigation in domains of high consumer lock-in

Ivanova

Vita

Wood

et al. 2018

Global Environmental Change

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As climate policy needs to address all feasible ways to reduce carbon emissions, there is an increasing focus on demand-side solutions. Studies of household carbon footprints have allocated emissions during production to the consumption of the produced goods, and provided an understanding of what products and consumer actions cause significant emissions. Social scientists have investigated how attitudes, social norms, and structural factors shape salient behavior. Yet, there is often a disconnect as emission reductions through individual actions in the important domains of housing and mobility are challenging to attain due to lock-ins and structural constraints. Furthermore, most behavioral research focuses on actions that are easy to trace but of limited consequence as a share of total emissions. Here we study specific alternative consumption patterns seeking both to understand the behavioral and structural factors that determine those patterns and to quantify their effect on carbon footprints. We do so utilizing a survey on consumer behavioral, attitudinal, contextual and socio-demographic factors in four different regions in the EU. Some differences occur in terms of the driving forces behind behaviors and their carbon intensities. Based on observed differences in mobility carbon footprints across households, we find that the key determining element to reduced emissions is settlement density, while car ownership, rising income and long distances are associated with higher mobility footprints. For housing, our results indicate that changes in dwelling standards and larger household sizes may reduce energy needs and the reliance on fossil fuels. However, there remains a strong need for incentives to reduce the carbon intensity of heating and air travel. We discuss combined effects and the role of policy in overcoming structural barriers in domains where consumers as individuals have limited agency.

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Section: Urban-rural Typology and Household Sizesupporting

confidence: 66%

Section: Mobilitymentioning

confidence: 99%

Carbon mitigation in domains of high consumer lock-in

Ivanova

Vita

Wood

et al. 2018

Global Environmental Change

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“…Older adults have higher depression prevalence and fewer total trips than younger adults. The pattern of total trips and active travel by income is consistent with empirical data (Quinn, Jakicic, Fertman, & Barone Gibbs, 2016) further validating our model. Specifically, F + L is the most effective scenario in decreasing depression prevalence, particularly among lower income groups.…”

Section: Resultssupporting

confidence: 87%

Examining the possible impact of daily transport on depression among older adults using an agent-based model

Yang

Langellier

Stankov

et al. 2018

Aging & Mental Health

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“…Based on consumer choice theory, travelers are assumed to rationally choose a transport mode to travel from their origins to their destinations by evaluating the characteristics of various available competing alternatives, and weighing their options in an attempt to maximize personal utility [4,37,38]. Individuals' transport mode choices and travel behaviors are affected by a complex set of factors, such as availability, travel costs, personal attitudes, personal demographics, habits, perceptions of safety and convenience, cultures, and built environments [39][40][41][42][43][44]. Ridesharing provides a more flexible, more convenient, and often faster option than public transit [14], and a lower cost than private cars (ridesharing riders could share some costs with drivers, and riders do not need to pay for ownership) [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

Zhang

2018

Sustainability

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To improve the sustainability and efficiency of transport systems, communities and government agencies throughout the United States (US) are looking for ways to reduce vehicle ownership and single-occupant trips by encouraging people to shift from driving to using more sustainable transport modes (such as ridesharing). Ridesharing is a cost-effective, sustainable and effective alternative transportation mode that is beneficial to the environment, the economy and society. Despite the potential effect of vehicle ownership on the adoption of ridesharing services, individuals’ ridesharing behaviors and the interdependencies between vehicle ownership and ridesharing usage are not well understood. This study aims to fill the gap by examining the associations between household vehicle ownership and the frequency and probability of ridesharing usage, and to estimate the effects of household vehicle ownership on individuals’ ridesharing usage in the US. We conducted zero-inflated negative binomial regression models using data from the 2017 National Household Travel Survey. The results show that, in general, one-vehicle reduction in households was significantly associated with a 7.9% increase in the frequency of ridesharing usage and a 23.0% increase in the probability of ridesharing usage. The effects of household vehicle ownership on the frequency of ridesharing usage are greater for those who live in areas with a higher population density than those living in areas with a lower population density. Young people, men, those who are unable to drive, individuals with high household income levels, and those who live in areas with rail service or a higher population density, tend to use ridesharing more frequently and are more likely to use it. These findings can be used as guides for planners or practitioners to better understand individuals’ ridesharing behaviors, and to identify policies and interventions to increase the potential of ridesharing usage, and to decrease household vehicle ownership, depending on different contextual features and demographic variables. Comprehensive strategies that limit vehicle ownership and address the increasing demand for ridesharing have the potential to improve the sustainability of transportation systems.

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Demographic factors, workplace factors and active transportation use in the USA: a secondary analysis of 2009 NHTS data

Abstract: Active transportation differed across demographic and workplace factors. These relationships could inform infrastructure policy decisions and workplace wellness programming targeting increased active transportation.

Cited by 18 publications

References 23 publications

Carbon mitigation in domains of high consumer lock-in

Carbon mitigation in domains of high consumer lock-in

Examining the possible impact of daily transport on depression among older adults using an agent-based model

Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

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