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Article
Car-less or car-free? Socioeconomic and mobility differences among zero-car households
Abstract
Transportation professionals have long identified the important division between choice and constraint in modal decision-making. However, while heterogeneity within some modal groups such as transit riders is well documented, intragroup differences in other groups have been largely ignored. In particular, significant heterogeneity exists among zero-car households, who may not own a car due to choice (car-free) or constraint (car-less). Recognition of intragroup heterogeneity among zero-car households yields policy implications as cities consider where to invest their ever more precious and more limited transportation resources. Using activity diary data from the 2012 California Household Travel Survey, I investigate two research questions: first, how do car-less households compare to car-free households in terms of both their socioeconomic characteristics and their relative share of zero-car households? Second, how do motivations behind not owning a car translate into mobility differences - including daily trip counts and miles traveled? I find that, contrary to media reports that the number of car-free households—that is, households that choose not to own a car—is “booming” 79 percent of zero-car households do not own a car because of economic or physical constraints. Car-less households in many ways mirror the captive transit population, in that they have significantly lower household incomes, lower educational attainments, and are disproportionately non-white compared to car-free households. Observed socioeconomic variations translate into mobility differences, with car-free travelers taking more trips and traveling more miles per day. Policy makers should consider extending access to carshare services, which are positively associated with more trips and miles traveled among both car-free and car-less households.
... Findings from previous literature (Blumenberg et al., 2018;Blumenberg and Pierce, 2012;Brown, 2017) inform the inclusion of the independent variables in our model specifications and are presented in Fig. 1. Individual characteristics (e.g., race, age, etc.) are determined based on the characteristics of the household respondent. ...
... Choices regarding household vehicle fleets may reflect constraint (financial, physical, or otherwise), choice (the decision to live car-free), or some combination of the two. Between choice and constraint explanations, constraints account for nearly 80 percent of carlessness among California households (Brown, 2017). While there are many constraints to automobile ownership, economic constraints are the largest (Mitra and Saphores, 2017). ...
... While there are many constraints to automobile ownership, economic constraints are the largest (Mitra and Saphores, 2017). One study shows that this class of constraints comprises more than half of the self-reported explanations for households not having a car in California (Brown, 2017). These constraints also likely play a role in explaining carlessness, as well as differences in trip-making levels (Murakami and Young, 1997), nationally. ...
Despite their negative externalities, cars provide many benefits. Chief among these is the ability to travel to destinations within a reasonable time budget. Consequently, in the U.S. most households—even low-income households—own and use automobiles. But technological innovations may be altering this dynamic. New technology-facilitated services and activities may reduce the advantages of private vehicle ownership, potentially allowing households to live car-free or downsize their household vehicle fleets. In this study we use data from the 2017 U.S. National Household Travel Survey to investigate the relationship between these innovations and vehicle ownership. We find a positive relationship between the use of ridehail and carshare services and the likelihood of being a zero-vehicle household. The data also show a positive relationship between online shopping and working from home and the likelihood of having fewer household vehicles than adults. Combined, the findings suggest that new technology-facilitated activities may allow some households to eliminate or reduce their dependence on privately-owned vehicles. For other households, new technology-facilitated services may not directly affect their decisions about automobile ownership, but rather increase their access to opportunities while easing the financial burden of vehicle ownership. Agencies and organizations should explore opportunities to better connect households—particularly households with travel and financial constraints—to technology-facilitated services and activities that enable improved access.
... Additionally, while carlessness is typically associated with poverty, some non-poor people make housing and employment choices that enable life without a car. Brown (2017) distinguishes between "car-less" and "car-free" households. Using 2012 California Household Travel Survey data, Brown estimates that 79 percent of zero-vehicle households were car-less-without a private vehicle due to financial or personal constraints-while 21 percent were car-free-without a private vehicle due to personal lifestyle choices (Brown, 2017). ...
... Brown (2017) distinguishes between "car-less" and "car-free" households. Using 2012 California Household Travel Survey data, Brown estimates that 79 percent of zero-vehicle households were car-less-without a private vehicle due to financial or personal constraints-while 21 percent were car-free-without a private vehicle due to personal lifestyle choices (Brown, 2017). As noted above, the car-free lifestyle may appeal to a generation that values dense urban living and physical proximity to activities, and has financial resources at their disposal (Moos, Pfeiffer, and Vinodrai, 2017). ...
... To examine these two types of zero-vehicle households, we draw on the recent study by Brown (2017) to create a simple (and admittedly imperfect) typology of census tracts: "car-less" neighborhoods, "car-free" neighborhoods, and other neighborhoods (See Figure 8-6). Car-less tracts host many poor people and many households with no vehicles. ...
Public transit ridership has been falling nationally and in California since 2014. The San Francisco Bay Area, with the state's highest rates of transit use, had until recently resisted those trends, especially compared to Greater Los Angeles. However, in 2017 and 2018 the region lost over five percent (>27 million) of its annual riders, despite a booming economy and service increases. This report examines Bay Area transit ridership to understand the dimensions of changing transit use, its possible causes, and potential solutions. We find that: 1) the steepest ridership losses have come on buses, at off-peak times, on weekends, in non-commute directions, on outlying lines, and on operators that do not serve the region's core employment clusters; 2) transit trips in the region are increasingly commute-focused, particularly into and out of downtown San Francisco; 3) transit commuters are increasingly non-traditional transit users, such as those with higher incomes and automobile access; 4) the growing job-housing imbalance in the Bay Area is related to rising housing costs and likely depressing transit ridership as more residents live less transit-friendly parts of the region; and 5) ridehail is substituting for some transit trips, particularly in the off-peak. Arresting falling transit use will likely require action both by transit operators (to address peak capacity constraints; improve off-peak service; ease fare payments; adopt fare structures that attract off-peak riders; and better integrate transit with new mobility options) and public policymakers in other realms (to better meter and manage private vehicle use and to increase the supply and affordability of housing near job centers).
... Additionally, while lack of vehicle ownership is typically associated with poverty, some people not in poverty make housing and employment choices that enable life without a car. Brown (2017) distinguishes between "car-less" and "car-free" households (See Appendix A, Section 2 for further definitions). Using 2012 California Household Travel Survey data, she estimates that 79 percent of zero-vehicle households in the state were car-less-without a private vehicle due to financial or personal constraints-while 21 percent were car-free-without a private vehicle due to personal lifestyle choices (Brown, 2017). ...
... Brown (2017) distinguishes between "car-less" and "car-free" households (See Appendix A, Section 2 for further definitions). Using 2012 California Household Travel Survey data, she estimates that 79 percent of zero-vehicle households in the state were car-less-without a private vehicle due to financial or personal constraints-while 21 percent were car-free-without a private vehicle due to personal lifestyle choices (Brown, 2017). We investigate this distinction further in Chapter 14. ...
... To examine these two types of zero-vehicle households, we draw on a study by Brown (2017) simple (and admittedly imperfect) typology of neighborhoods: "car-less" census tracts, "car-free" tracts, and other tracts (See Appendix A, Section 2). Car-less neighborhoods host many people in poverty and many households with no vehicles. ...
Transit patronage plunged staggeringly, from 50 to as much as 94 percent, during the first half of 2020 amidst the worst global pandemic in a century. But transit’s troubles in California date much earlier. From 2014 to 2018, California lost over 165 million annual boardings, a drop of over 11 percent. This report examines public transit in California in the 2010s and the factors behind its falling ridership.
We find that ridership gains and losses have been asymmetric with respect to location, operators, modes, and transit users. Transit ridership has been on a longer-term decline in regions like Greater Los Angeles and on buses, while ridership losses in the Bay Area are more recent. While overall transit boardings across the state are down since 2014, worrisome underlying trends date back earlier as patronage failed to keep up with population growth. But reduced transit service is not responsible for ridership losses, as falling transit ridership occurred at the same time as operators instead increased their levels of transit service.
What factors help to explain losses in transit ridership? Increased access to automobiles explains much, if not most, of declining transit use. Private vehicle access has increased significantly in California and, outside of the Bay Area, is likely the biggest single cause of falling transit ridership. Additionally, new ridehail services such as Lyft and Uber allow travelers to purchase automobility one trip at a time and likely serve as a substitute for at least some transit trips. Finally, neighborhoods are changing in ways that do not bode well for public transit. Households are increasingly locating in outlying areas where they experience longer commutes and less transit access to employment. At the same time, a smaller share of high-propensity transit users now live in the state’s most transit-friendly neighborhoods.
While the 2010s proved a difficult decade for public transit in California, and the opening of the current decade has been an even bigger challenge, transit remains an essential public service. Effectively managing transit recovery in California will require a clear-eyed understanding of the substantially altered environment within which these systems large and small must now operate.
... Dense, compact, connected, diverse, safe, and both physically and socio-economically accessible urban areas and transportation networks provide the basis for a shift towards sustainable and active mobility, and the increased use of sustainable modes of transportation as alternatives to the private car (Sallis et al. 2016;Nieuwenhuijsen and Khreis 2016). A carless lifestyle can be used as an indicator of accessible and well-connected areas where an active, equitable and sustainable alternative to the use of private car is provided for daily travel (Nieuwenhuijsen and Khreis 2016;Brown 2017;Banister 2018). Newman et al. (2016) have advanced this linkage between the features of the physical urban environment and different mobility lifestyles by defining three urban environment types, urban fabrics, that support either a carless lifestyle (walking urban fabric, and transit fabric) or a cardependent lifestyle (automobile fabric). ...
... When studying car ownership, and carlessness in particular, the dichotomy of carlessness cannot be overlooked. It refers to voluntary and involuntary carlessness, or car-free and carless citizens as coined by Brown (2017), where individuals or households either choose to go carless, or cannot, for example, afford a car in which case carlessness is not a choice (Brown 2017;Mitra and Saphores 2017). While car ownership and its links to the urban environment have been widely studied in academic literature-and the urban environment and socio-demographic characteristics of carlessness to some extent-combinations of spatial and socio-demographic variables as determinants for car ownership and carlessness in urban areas remain under researched (Banister 2018;Mitra and Saphores 2017). ...
... When studying car ownership, and carlessness in particular, the dichotomy of carlessness cannot be overlooked. It refers to voluntary and involuntary carlessness, or car-free and carless citizens as coined by Brown (2017), where individuals or households either choose to go carless, or cannot, for example, afford a car in which case carlessness is not a choice (Brown 2017;Mitra and Saphores 2017). While car ownership and its links to the urban environment have been widely studied in academic literature-and the urban environment and socio-demographic characteristics of carlessness to some extent-combinations of spatial and socio-demographic variables as determinants for car ownership and carlessness in urban areas remain under researched (Banister 2018;Mitra and Saphores 2017). ...
Diverse physical features of urban areas alongside socio-demographic characteristics affect car ownership, and hence the daily mobility choices. As a case of sustainable mobility, we explore how various urban environments and socio-demographics associate with the spatial and social distribution of household car ownership and carlessness in the Helsinki Metropolitan Area, Finland. Three urban fabrics characterizing the study area are established based on the transportation mode (walking, public transportation, or automobile) the physical urban environment primarily supports. The national level Monitoring System of Spatial Structure and Urban Form database, and the National Travel Survey (2016) are utilized to further include spatial and socio-demographic variables into our analysis across these fabrics. Our results show that households with and without cars differ in terms of residential distance to the city center, neighborhood density, house type, and socio-demographic profiles. Single pensioners and students are most likely to be carless, whereas families represent the opposite. Within the carless households the differences are also evident between different groups. For the more affluent households residing in dense and well-connected areas, and mostly possessing driver’s licenses, carlessness is presumably a choice. Contrarily, many other carless households represent the less affluent often located in the more distant, low-density, and less accessible areas, while also possessing less driver’s licenses, making carlessness more of a constraint, as the local urban fabric does not support such lifestyle. Consequently, carless households should be increasingly recognized as a focus group in sustainable urban planning in terms of identifiable best practices and potential vulnerability.
... Most carless households live in these transit-poor communities, at least in the United States (A. E. Brown, 2017). In Canada, the number of zero-car households is increasing in the suburbs (Allen & Farber, 2020b). ...
... We add to a growing evidence base that illustrates how simplistic characterizations of carless households, and only carless households, as "transit dependent" are not useful for equity planning (A. E. Brown, 2017Brown, , 2019Lovejoy & Handy, 2008). ...
Problem, research strategy, and findings
Millions of North Americans stopped riding public transit in response to COVID-19. We treat this crisis as a natural experiment to illustrate the importance of public transit in riders’ abilities to access essential destinations. We measured the impacts of riders forgoing transit through a survey of transportation barriers completed by more than 4,000 transit riders in Toronto and Vancouver (Canada). We used Heckman selection models to predict six dimensions of transport disadvantage and transport-related social exclusions captured in our survey. We then complemented model results with an analysis of survey comments describing barriers that individuals faced. Lack of access to alternative modes is the strongest predictor of a former rider experiencing transport disadvantage, particularly neighborhood walkability and vehicle ownership. Groups at risk of transport disadvantage before COVID-19, particularly women and people in poorer health, were also more likely to report difficulties while avoiding public transit. Barriers described by respondents included former supports no longer offering rides, gendered household car use dynamics, and lack of culturally specific or specialized amenities within walking distance.
Takeaway for practice
Policymakers should plan for a level of redundancy in transportation systems that enables residents to access essential destinations when unexpected service losses occur. Designing communities that enable residents to walk to those essential destinations will help reduce the burdens faced by transit riders during crises that render transit unfeasible. At the same time, planners championing active travel as an alternative to transit during such crises also need to devise solutions for former transit riders for whom active travel is ill suited, for example, due to physical challenges with carrying groceries or needing to chaperone children.
... Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most likely to be affected financially by the crisis but are also the least likely to own their own cars [3]. Therefore most rely on the public transit system to get to work, school or access child services. ...
... A major reason low-income groups are heavily reliant on public transportation is their likelihood of owning a personal vehicle. According to an analysis of 2012 California Household Travel Survey data, 78% of households without a car do not have a car as a result of economic or physical barriers [3]. Together, these studies suggest that individuals of a lower socioeconomic background may be disproportionately impacted by changes in public transit availability. ...
Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus disease COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19's affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9% and 65.1% from 2019 baselines before stabilizing at 48.4% and 42.8% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs 58%) in Nashville.
... Second, if the ridesourcing companies provide shared ridesourcing services like Lyft line and uberPool in these two cities, it would decrease the service fare and become more affordable for lower-income households. Brown (2017) showed that in Los Angeles, the ridesourcing services were used more in lower-income areas, with a higher percentage of families with no cars [60]. However, some social perceptions of shared ridesourcing should be considered, such as women may not prefer to share their ridesourcing with strangers in these two cities as it is reported in the other similar studies in Brazil and the American cities [17,61,62]. ...
... Second, if the ridesourcing companies provide shared ridesourcing services like Lyft line and uberPool in these two cities, it would decrease the service fare and become more affordable for lower-income households. Brown (2017) showed that in Los Angeles, the ridesourcing services were used more in lower-income areas, with a higher percentage of families with no cars [60]. However, some social perceptions of shared ridesourcing should be considered, such as women may not prefer to share their ridesourcing with strangers in these two cities as it is reported in the other similar studies in Brazil and the American cities [17,61,62]. ...
Despite the growing share of ridesourcing services in cities, there is limited research about their impacts on other transport mode choices in the large cities of the Middle East and North Africa (MENA). There is a debate about whether ridesourcing affects the frequent use of sustainable modes like public transport. This study uses the results of a large-scale series of face-to-face interviews in Tehran and Cairo to study the relationship between the regular use of ridesourcing and the frequency of public transport use. Descriptive statistics and logit regression are used to analyze this association. The findings indicate contradictory correlations between the regular use of ridesourcing and the frequent use of public transport in Tehran and Cairo. The regular use of ridesourcing has a positive correlation with the probability of frequent public transport use in Cairo. In contrast, this correlation is negative in Tehran, which means that the regular ridesourcing users are less likely than the non-regular users to use frequently public transport. The reasons for these different correlations are studied in terms of socioeconomic variables, accessibility, and the citizens' perception of public transport in both cities. Keywords: ridesourcing in the MENA region; public transport; transport mode choice analysis; impacts of ridesourcing on mobility behavior
... Furthermore, shared ride-hailing is acknowledged as much more economical than ride-hailing, since the cost of the trip is split among the riders (Chen, Zheng, Wang & Chen, 2018), and potentially impacts the car ownership, since it fills the gap between a private taxi and public transport. Brown (2017) and Shaheen and Cohen (2007) reported that people have started forgoing car ownership and utilising the available resources instead for buying a new form of automobility, such as ride-hailing or ridesourcing. Brown (2017) asserted that young Americans are embracing a car-free lifestyle to achieve sustainability and congestion relief goals by shifting to new forms of mobilities. ...
... Brown (2017) and Shaheen and Cohen (2007) reported that people have started forgoing car ownership and utilising the available resources instead for buying a new form of automobility, such as ride-hailing or ridesourcing. Brown (2017) asserted that young Americans are embracing a car-free lifestyle to achieve sustainability and congestion relief goals by shifting to new forms of mobilities. However, in spite of certain benefits which are associated with the uncertainties and risks involved in shared ride-hailing services due to the anonymity of fellow passengers (Moody, Middleton & Zhao, 2019), several studies have pointed out that riders are less likely to share rides with racially and ethnically diverse passengers (Brown, 2018). ...
Manuscript type: Research paper Research aims: This study aims to investigate the factors influencing shared ride-hailing services, amongst commuters in India. Design/Methodology/Approach: Data collected across 355 respondents were analysed using structural equation modelling (SEM). Research findings: The findings reveal that all the four factors, i.e., the perceived use, perceived ease of use, trust and perceived risk, significantly influence commuter's intentions to use shared ride-hailing service. Gender is found to moderate the relationship between perceived use, and the intention to use, as well as perceived risk and intention to use. This study however, does not find any empirical evidence to establish the moderation impact of car ownership on the relationship between perceived risk and the intention to use. Theoretical contribution/Originality: This study expands the previous literature by incorporating trust and perceived risk in examining the intention to use shared ride-hailing services. It also supplements previous works by examining gender and car ownership as the moderating variable. Practitioner/Policy implication: The findings suggest that marketers need to focus on strategies to reduce the risks associated with using shared ride-hailing services, especially amongst women riders.
... One solution to improve the positive impacts of ridesourcing on urban sustainability is developing and increasing the availability of shared ridesourcing services like the uber-Pool and Lyft line in New York, which increase the passenger per car travels decrease VKT (vehicle kilometers traveled) and traffic congestion. However, there might be some social and cultural barriers for shared ridesourcing, like the preference for privacy, or women might not prefer to share their ridesourcing services with strangers [46,87,93], which should be considered to develop these mobility services. ...
The energy consumption and emissions in the urban transportation are influenced not only by technical efficiency in the mobility operations but also by the citizens' mobility behaviors including mode choices and modal shift among sustainable and unsustainable mobility modes. Information and Communication Technologies (ICTs) can play an important role in the mobility behaviors of citizens, and it is necessary to study whether ICTs support sustainable mode choices like public transport and nonmotorized modes, which increase the total energy efficiency in the urban mobility and reduce traffic congestion and related emissions. This paper focuses on the two most popular ICT services in the urban transport, which are ATIS (Advanced Traveler Information Systems), and ridesourcing services. This study used the New York Citywide Mobility Survey (CMS) findings with a sample of 3346 participants. The associations between using these two ICT services and the mobility behaviors (mode choice with ATIS and modal shift to ridesourcing) are analyzed through a multinomial logistic regression and descriptive statistics, and the results are compared with similar international studies. The findings indicate that the respondents who use ATIS apps more frequently are more likely to use rail modes, bicycles, bus/shuttles, and rental/car sharing than private cars for their work trips. Moreover, the findings of the modal shift to ridesourcing indicate that the most replaced mobility modes by ridesourcing services are public transport (including rail modes and buses), taxis, and private cars, respectively.
... For example, among car-free Californians who do not own a car because of environmental beliefs, about 39% held a carshare membership as of 2012, compared to just 3.6% of all zero-car households. Nonetheless, these other zero-car households (many of whom do not own cars due to economic circumstances) still take nearly one-quarter (22%) of trips by car, suggesting they still require at least intermittent car access and gain it in other ways (34). Previous research shows that immigrants, for example, are far more likely to have established a carpool-both within and between households-compared to native-born travelers (35). ...
COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and—for those who cannot work from home—to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode—ride-hailing—fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.
... Successful duplication of known relationships is an emerging approach to establishing the utility of online convenience samples (Boas et al., 2020). Vehicle ownership was specifically chosen for validation as it has traditionally served as a primary measure for defining whether an individual is transit dependent (Brown, 2017). The authors attempted to repeat this process in Vancouver but were not permitted access to comparable travel survey data. ...
Background
During the COVID-19 pandemic, many urban residents stopped riding public transit despite their reliance on it to reach essential services like healthcare. Few studies have examined the implications of public transit reliance on riders’ ability to reach healthcare when transit is disrupted. To understand how shocks to transportation systems impact healthcare access, this study measures the impact of avoiding public transit on the ability of riders to access healthcare and pharmacy services during lockdowns.
Methods
We deployed a cross-sectional survey of residents of Toronto and Vancouver in May 2020 through Facebook advertisements and community list-serves. Eligibility criteria included riding transit at least weekly prior to the pandemic and subsequent cessation of transit use during the pandemic. We applied multivariable modified Poisson models to identify socio-demographic, transportation, health-related, and neighborhood predictors of experiencing increased difficulty accessing healthcare and getting prescriptions while avoiding public transit. We also predicted which respondents reported deferring medical care until they felt comfortable riding transit again.
Results
A total of 4367 former transit riders were included (64.2% female, 56.1% Toronto residents). Several factors were associated with deferring medical care including: being non-White (Toronto, APR, 1.14; 95% CI, 1.00-1.29; Vancouver, APR, 1.52; 95% CI, 1.26-1.84), having a physical disability (Toronto, APR, 1.20; 95% CI, 1.00-1.45; Vancouver, APR, 1.42; 95% CI, 1.08-1.87), having no vehicle access (Toronto, APR, 1.74; 95% CI, 1.51-2.00; Vancouver, APR, 2.74; 95% CI, 2.20-3.42), and having low income (Toronto, APR, 1.77; 95% CI, 1.44-2.17; Vancouver, APR, 1.51; 95% CI, 1.06-2.14).
Discussion
During COVID-19 in two major Canadian cities, former transit riders from marginalized groups were more likely to defer medical care than other former riders. COVID-19 related transit disruptions may have imposed a disproportionate burden on the health access of marginalized individuals. Policymakers should consider prioritizing healthcare access for vulnerable residents during crises.
... Unlike the car-less, whose attitudes and behaviours have been researched in the context of transport poverty, transport disadvantage, and gender inequality (Lucas et al. 2016;Pojani et al. 2017;Johnson 2020), car-free people are underrepresented in the planning literature. The statistical breakdown of the 'car-less' vs 'carfree' population is unavailable, but, based on our understanding of the local context, the carfree are a minority (see also Brown 2017). Hence, a qualitative method, based on in-depth interviews, was deemed appropriate to explore the situation. ...
This exploratory study examines the motivations of people who live “car-free” by choice in Brisbane, Australia. The Theory of Planned Behaviour is employed as a guiding framework for the analysis. Unlike the “car-less”, the “car-free” are physically and intellectually able to drive, and have sufficient income to purchase and maintain a car, yet they have chosen on shun automobility. In a low-density, sprawling, and car-oriented context like Brisbane’s, their choice is unusual, hence worth examining. Through in-depth interviews of 24 car-free people, we found that they have embraced car-free living in order to ‘go green’; pursue health and well-being; and achieve convenience and minimalism. Personal advantages have priority over collective interests. Participants share a belief that car ownership is simply unnecessary if one makes certain practical adjustments. A characteristic element that unites the car-free is their maverick outlook. All have been willing to defy a key societal norm, automobility, even as this involves some downsides. They believe that the advantages of a car-free life outweigh any drawbacks. It appears that a car-free lifestyle is an educated middle-class phenomenon. However, a larger survey may reveal specific demographic clusters among the car-free.
... The most important factor influencing whether a household owns a car is its level of income. In the U.S., most households that can afford to own a car do so (Blumenberg and Pierce, 2012;Brown, 2017;Chu, 2002;Giuliano and Dargay, 2006;Mitra and Saphores, 2017). Income may be the main factor, but the built environment, transit service, and land use policies all affect car ownership levels (Chatman, 2013;Guo, 2013;Salon, 2009;Weinberger, 2012;Zegras, 2010). ...
Our study explores the phenomenon of using the crowdfunding website GoFundMe.com to raise money to purchase and or repair a personal vehicle. We use both text mining and qualitative analysis methods to ask three research questions about GoFundMe campaigns from the United States created in 2018 and 2019. First, how do campaigners on GoFundMe describe their need for personal vehicles? Second, what precipitating events led them to seek crowdfunding for a car purchase or repair? Third, what are broader contexts for these requests for financial assistance? We find that many campaigns describe several interrelated crises, complications, or challenges that the campaigners faced, such as medical conditions, loss of employment, and housing crises. Most campaigners are without a functioning car because of an unexpected event—a car crash, a breakdown, or repairs piling up—combined with their inability to pay for the repairs or replacement. Finally, campaigners wanted cars to travel to work and school, chauffeur children, and access medical care.
... Further important factors are the place of residence (McGoldrick and Caulfield, 2015;Picard et al., 2018;Verma et al., 2016), the political and legal framework (de Jong et al., 2009;Yang et al., 2017) and car ownership itself as it appears to trigger an inertia effect (Dargay and Hanly, 2007;Kitamura, 2009;Roorda et al., 2009). Finally, the most important reasons for not owing a private car are good availability of public transport, living in a single household or in a household with no children (Oakil et al., 2016), low income (Klein and Smart, 2017) and environmental reasons (Brown, 2017). ...
Private car ownership provides the foundation for the current dominance of the car in the modal split of many cities, regions and countries. A private car is seen by many as the fastest, safest and most comfortable means of transportation. Hence private car ownership is further increasing in both the emerging markets of development countries as well as in the saturated markets of industrial nations. Yet, the increasing number of cars leads to negative consequences such as more congestion, pollution, and inefficient land use so that many cities all around the world have introduced different measures to reduce the use and ownership of private cars.
This paper explores whether the introduction of automated vehicles can help to reduce private car ownership through new mobility services or whether it will make the ownership of a private car even more attractive and thus further increase sales. The data basis is comprised of several focus group discussions with residents from different spatial regions of Berlin and its surroundings and different sociodemographic backgrounds.
Our results indicate that the automation of vehicles could make the possession of a private car even more attractive to current car owners and also contribute to the emergence of completely new groups of car owners. However, our findings also point out how the particular features of both automated private cars as well as new mobility services enabled through automated vehicles could lead to a reduction in private car ownership.
In fact, whether the introduction of automated vehicles and new mobility services leads to an increase or a reduction in private car ownership seems to depend heavily on the concrete implementation. This suggests that there is plenty of room for car manufacturers, ride-sharing operators, and policy-makers at local, regional and state-level to shape the implementation into a direction beneficial for society.
... While the majority of U.S. households own at least two vehicles, over 10 million households do not have access to a car. The vast majority of these households do not have a vehicle due to physical or economic constraints (Brown, 2017), including over 25% of households earning less than $25,000 per year (U.S. Census Bureau, 2020). Over half of families living in poverty do not have access to a vehicle at least some of the time (Klein & Smart, 2017), which limits access to a range of essential services like jobs, health care, and food (Blumenberg & Pierce, 2017;Bullard et al., 2004;Dawkins et al., 2015). ...
This analysis finds that cost reductions in new electric vehicles (EVs) will lead to decreased used EV prices and cost parity with used gasoline vehicles for low-income households in the 2025-2030 time period.
Higher rates of depreciation for first owners of EVs will lead to larger benefits for lower-income second owners. By 2029, EVs will reach upfront price parity with the average vehicle purchased by a low-income household, less than two years after the average vehicle purchased by a high-income household. Currently, once accounting for fuel and other operating savings, some households in all income groups could save money by replacing at least one vehicle with an EV; this increases to 45% of households by 2025 and 95% of households by 2030.
Savings from EVs relative to income are significantly higher for low-income households, non-White households, and households in areas with higher levels of pollution. For car owners in the lowest-income quintile, savings from switching to EVs amount to $1,000 per household annually, or 7% of income, by 2030.
Even with widespread EV affordability, additional policy action would ensure equal access to EVs. Previous studies have shown that low-income EV buyers are more responsive to incentives, and that purchase incentives have become more important over time. Combustion vehicle phase-out regulations can force manufacturers to serve diverse markets, and broader access to financing for EVs will be critical for low-income households. In addition, policymakers will need to ensure targeted deployment of home and public charging deployment to support vulnerable communities and renters with less charging access.
... Meanwhile, little is known about the transit-based healthcare accessibility for those car-free households in the context of the United States. Furthermore, due to the discrimination and segregation history in many major cities in the United States such as Chicago, non-White residents, who tend to be of low income and are less likely to own a car (Brown 2017), have largely been confined to reside in the central city where the public transit density tends to be higher (Liu and Kwan 2020). The spatial confinement of non-White minorities to the central city and the sprawl of job opportunities to the suburban regions where transit density is low have led to lower job accessibility for the central city minorities, which has been called the spatial mismatch between jobs and housing (Kain 1968). ...
Public transit is indispensable for car-free households to access healthcare. Meanwhile, different households have unequal transit-based healthcare accessibility due to different socio-economic factors such as race/ethnicity and car ownership. Few studies have comprehensively explored the inequality in transit-based healthcare accessibility by integrating both racial/ethnic inequity and car ownership. This study fills the gap with an up-to-date analysis of transit-based healthcare accessibility in the Chicago Metropolitan Area at the census tract level. The results show that the percentage of car-free households is positively related to transit-based healthcare accessibility; while a higher percentage of minorities (i.e., Black/Hispanic) is negatively related to transit-based healthcare accessibility. Among all neighbourhoods with higher percentages of higher-than-average car-free households, Hispanic-majority neighbourhoods fare the worst; while White-majority neighbourhoods have much better healthcare accessibility than both Black-majority and Hispanic-majority neighbourhoods. © 2021 The Authors. The Canadian Geographer / Le Géographe canadien published by Wiley Periodicals LLC on behalf of Canadian Association of Geographers / l'Association canadienne des géographes
... It is also intuitive that higher-income households are more likely to transition into car ownership than their low-income counterparts. For wealthier households, carlessness is often a choice, reflecting both a desire and an ability to reside in walkable, transit-rich neighborhoods; for poorer households, by contrast, carlessness is generally due to financial constraints, and not the result of a desire for a "car free" lifestyle (Brown 2017). Consequently, when relocating to a suburban neighborhood-where vehicle ownership is crucial for maintaining adequate mobility and destination access-higher-income households are better able to adjust to their new environment than those with limited resources. ...
While the relationship between automobile ownership and the built environment is well established, less is known about how household relocations—specifically, moves between urban and suburban geographies—affect the likelihood of owning an automobile. Using the Panel Study of Income Dynamics and a refined neighborhood typology, I examine the relationship between inter-geography moves and transitions into and out of carlessness. Results suggest that among low-income households, urban-to-suburban movers have an increased likelihood of becoming car owners; those moving in the “opposite” direction—from suburban to urban neighborhoods—show a high propensity to transition into carlessness. Patterns among higher-income households, while similar, are more pronounced. In particular, higher-income carless households that make urban-to-suburban moves are far more likely to become car owners than their low-income counterparts. This highlights the ease with which higher-income households adjust their car ownership levels to suit their post-move neighborhoods. Higher-income suburban-to-urban movers are also more likely to transition into carlessness than low-income households. Importantly, however, only households at the bottom end of the “higher income” distribution have an increased propensity to become carless; suburban-to-urban movers with more financial resources maintain vehicle ownership rates similar to households that remain in the suburbs.
... Inroads to this research agenda have emerged through 'car-free' discourses, which imagine life in cities with little and limited car-use (Topp & Pharoah, 1994). Although early understandings of the term 'car-free' were synonymous with being 'car-less', and therefore, mobility-constrained, being car-free is now promoted as an example of sustainable consumption, signalling liberation from car ownership dependence (Brown, 2017). The discourse of 'car-free' has become part of a larger movement promoting pedestrian zones, more public recreational space, and liveable cities (Ornetzeder et al., 2008). ...
The future of sustainable urban mobilities is a topic that is of increasing around the world.
Dominant discussions focus on what these future urban mobilities will constitute and
whether they will achieve targets to successfully decarbonise and to minimise the effects of climate change. Historically, transportation in our societies has mainly been approached from a positivist worldview that has attempted to understand the future through predictive models based on historical trends and relational extrapolation. However, the ambiguity created by the emergence of new transport innovations, such as self-driving cars, micromobility, battery-electric vehicles, and digital services, has created a high level of uncertainty about the future.
This Industrial PhD thesis takes this challenge as a starting point. First, the thesis rejects
the dominance of positivistic transport-planning approaches within the urban mobility
discourse. Second, it explores how to deploy architectural modes of enquiry and tools of
visualisation to develop novel methods for the development of sustainable mobilities for the future. A ‘research-through-design’ methodology is employed to incorporate architectural spatial knowledge and methods into mobilities research. This methodology is elaborated through a programmatic design research approach that generates knowledge on sustainability transitions in mobility through the development of design experiments that utilise mixed, and transdisciplinary methods.
... For example, it has long been understood that some public transit users have no other option while others freely choose to ride (Garrett and Taylor, 1999;Taylor and Morris, 2015). Other work has differentiated between car-less (constrained) and car-free (choice) households using responses to attitudinal questions on a household activity survey (Brown, 2017). That work has demonstrated both socioeconomic as well as travel behavioral differences between households that choose to not have a vehicle and those that wish to but cannot. ...
Public transit ridership forecasts have long played a role in understanding the potential success of a policy or investment, but their limitations have led researchers and practitioners to identify other performance analysis approaches. Accessibility, or the ease of reaching opportunities, has become very popular and widely used for this purpose. But commonly used accessibility measures also embody weaknesses that are seldom acknowledged; these limit their utility for truly understanding the benefits of transit investments. In this paper, we identify the pros and cons of these competing approaches and offer a third strategy. Specifically, we describe how revealed travel behavior data, potentially combined with near-term forecasts, can provide information about how current public transit users will be affected by a new project. While acknowledging the limitations of this approach, we demonstrate how accessibility can be misleading when applied without an understanding of ridership patterns.
... An examination of the empirical evidence indicates that car-ownership plays an important role in the usage pattern of alternative transportation modes (e.g., bicycle, e-scooter, or public transportation) (Van Acker and Wiltox, 2010;Fishman et al., 2014;Tao et al., 2019). Previous studies find that carless households are more likely to rely on alternative transportation modes to fulfill daily travel needs (Brown, 2017;Mitra and Saphores, 2020). To capture the effects of car ownership on e-scooter usage, this study includes the number of zero car households in each census tract with the expectation that a census tract with a higher number of zero car households will generate more e-scooter trips. ...
The rapid popularity growth of shared e-scooters creates the necessity of understanding the determinants of shared e-scooter usage. This paper estimates the impacts of temporal variables (weather data, weekday/weekend, and gasoline prices) and time-invariant variables (socio-demographic, built environment, and neighborhood characteristics) on the shared e-scooter demand by using four months (June 2019- October 2019) period of data from the shared e-scooter pilot program in Chicago. The study employs a random-effects negative binomial (RENB) model that effectively models shared e-scooter trip origin and destination count data with over-dispersion while capturing serial autocorrelation in the data. Results of temporal variables indicate that shared e-scooter demand is higher on days when the average temperature is higher, wind speed is lower, there is less precipitation (rain), weekly gasoline prices are higher, and during the weekend. Results related to time-invariant variables indicate that densely populated areas with higher median income, mixed land use, more parks and open spaces, public bike-sharing stations, higher parking rates, and fewer crime rates generate a higher number of e-scooter trips. Moreover, census tracts with a higher number of zero-car households and workers commuting by public transit generate more shared e-scooter trips. On the other hand, results reveal mixed relationships between shared e-scooter demand and public transportation supply variables. This study's findings will help planners and policymakers make decisions and policies related to shared e-scooter services.
... In the context of discussing the problem, two important issues should be distinguished: whether the lack of car ownership CAR-FREE HOUSING ESTATE is due to choice or to a limitation (financial or lack of parking space). The following are the results of a study [3] conducted in 2012 in California. The aim of the study was to identify and divide car-free households into two groups: car-less and car-free. ...
The number of cars in Polish cities increases every year. Despite the coexistence of other means of transport, such as public transport or cycling - the car is still the favorite means of transport for many residents. The significant increase in the number of registered cars, which can be observed consistently since the beginning of the 1990s, has left its mark throughout the city, especially in the human residence zone. Car-free housing estates are an alternative. Residents consciously give up parking space deciding on the city's communication system. The main purpose of the article is to analyse the system of functioning of car-free settlements in Europe and in the world as well as to determine the factors favoring such investments. The complementary goal is to present the possibilities of implementing this type of housing estates in Polish cities, taking into account possible social, planning and spatial barriers.
... below the federal poverty line (the poor) and the not non-Hispanic White minority (the minority). Previous transportation research has found that the poor and the minority are more troubled by low car ownership and a lack of available and reliable car-free alternatives when accessing employment opportunities (Bastiaanssen, Johnson, and Lucas 2020;A. E. Brown 2017). By investigating the opportunity of scooter use for these populations, this study examined the potential (in)equitable access to E-scooter by these economically challenged populations. The second type was people who are more physically disadvantaged, including people with ambulatory difficulties (the disabled) and the population aged 6 ...
This study revealed the inequitable societal impacts of E-scooters on disadvantaged populations. The study conducted a population distribution analysis to compare the use opportunities and space intrusion burdens of E-scooter sharing on four vulnerable population groups in Austin, Texas. Nearly all minority population experienced fewer E-scooter use opportunities. Ten percent of the minority population waited for a disproportionately longer time before a disturbance could be resolved. Ten percent of the low-income population were in a disadvantaged position in high opportunities and moderate burdens. Twenty percent of the physically disabled population faced more moderate-level burdens. The result did not show significant inequitable outcomes for the elderly population.
... Predominantly young people, families, and people with higher education qualifications are residents of car-reduced housing developments (Baehler, 2019;Baehler and Rérat, 2020b;Nobis, 2003;Scheurer, 2001). This shows the potential of carreduced neighborhoods to encourage car independence, as especially families and people with a higher education and higher income are usually more likely to use motorized transport (Brown, 2017;Johansson et al., 2019;Kühne et al., 2018;Mitra and Saphores, 2017;Villeneuve, 2017). Quantitative (Nobis, 2003;Scheurer, 2001), mixed methods (Baehler, 2019;Johansson et al., 2019) as well as qualitative studies (Sprei et al., 2020) conclude that, compared to the city as a whole, residents of such neighborhoods are significantly more likely to travel by non-motorized, public transport or sharing modes than by a private car. ...
Lately, transport researchers and practitioners are showing renewed interest in car-reduced neighborhoods and their residents’ mobility to investigate possible factors influencing sustainable transport. With a biographically inspired practice-theoretical approach, this study considers the ‘context of travel behavior’ and, thus, focuses on mobility as a ‘practice’ in order to improve the understanding of everyday mobility as well as the potential and limitations of implementing car-reduced housing. Based on qualitative interviews with residents of two German car-reduced neighborhoods, we first identify different compositions of materials, competences, and meanings (including the feelings and emotions) of car-(in)dependent mobility practices. Second, we discover the personal, social, temporal, and socio-structural circumstances of the residents’ travel behavior alongside ‘practice bundles’ that interact with car-(in)dependent mobility. Finally, our findings indicate, on the one hand, that the car-centric material context outside car-reduced neighborhoods, the incorporation of private car driving with the practice of everyday life, and the affective satisfaction with car use and ownership negatively influence car independence. On the other hand, our results highlight that residential location and its materiality in the case of car-reduced housing developments, as well as the personal-temporal and socio-cultural contexts of their residents’ mobility practices stabilize and support car independence and low-carbon mobility.
... Transportation hardship is rarely included in studies of material hardship despite demonstrated associations between car ownership and well-being related to employment, housing, or neighborhood quality (Bastiaanssen et al. 2020;Blumenberg and Pierce 2017;Jeon et al. 2018). Although car ownership may not perfectly capture transportation access, car deficit (i.e., less than one car per adult per household) is a proxy of scarce household resources (Blumenberg et al. 2020) that is more often due to financial constraints than personal choice (Brown 2017;Klein and Smart 2017). Car deficit households, particularly those that are also low-income, must negotiate the use of household vehicles when other forms of transit are too expensive or not available. ...
One in three U.S. households has experienced material hardship. The inadequate provision of basic needs, including food, healthcare, and transportation, is more typical in households with children or persons of color, yet little is known about material hardship in rural spaces. The aim of this study is to describe the prevalence of material hardships in Iowa and examine the relationship between rurality, income, and material hardship. Using data from the 2016 State Innovation Model Statewide Consumer Survey, we use logistic regression to examine the association between rurality, income, and four forms of material hardship. Rural respondents incurred lower odds than non‐rural respondents for all four hardship models. All four models indicated that lower income respondents incurred greater odds for having material hardship. Material hardship was reported across all groups, with rurality, income, race, and age as strong predictors of material hardship among our sample.
... Using data from the California Household Travel Survey, their study found that low-income autodeficit households manage their travel needs by carefully negotiating the use of their privately owned vehicles, which restricts their mobility benefits. Further differentiation between choice and constraint among zero-vehicle households was examined by Brown (2017), who reported that 79% of zero-vehicle households do not own a car because of economic or physical constraints. A study in Australia by Delbosc and Currie (2012) implied that restricted and unfulfilled mobility needs due to financial constraints in purchasing vehicles would lead to activity space restriction, which in turn might cause lower psychological well-being and fewer social support networks. ...
Emerging transportation technologies like autonomous vehicles and services like on-demand shared mobility are casting their shadows over the traditional paradigm of vehicle ownership. Several countries are witnessing stagnation in overall car use, perhaps due to the
proliferation of access-based services and changing attitudes of millennials. Therefore, it becomes necessary to revisit this paradigm, and reconsider strategies for modeling vehicle availability and use in this new era. This thesis attempts to do that through three studies
that contribute to the methodological, conceptual, and praxis literatures. The first study proposes a hybrid modeling methodology that leverages machine learning techniques to enhance traditional behavioral discrete choice models used in practice. The usefulness of this model to predict market shares of unforeseen choices like new mobility services is illustrated through an application to the off-peak car in Singapore. Our model significantly improves upon the market shares predicted by traditional models through an average reduction of 60% in RMSE. The second study shifts the focus from vehicle ownership to vehicle availability in the form of mobility bundles. We leverage Singapore’s unique policy environment to empirically model households’ preferences for unique mobility bundles that are constructed in an ordinal fashion. This is followed by an examination of car usage within the household. Significant intra-household interaction effects are found with respect to job location, in addition to the observation of gender biases in the decision-making process. The third study evaluates the effectiveness of car-lite policies that seek to replace private vehicle usage with shared and smart mobility services. Behavioral responses to the policy and associated market effects are modeled using an integrated land use transport simulator calibrated for Singapore. Initially favorable aggregate outcomes tend to disappear as short-term market effects set in. Although outcomes stabilize to a certain extent over the long-term, the initial characteristics of the study area are found to strongly influence the success of such policies.
Extensive research has found that people are more likely to choose the transport alternative which offers shorter travel time. But few studies approached the role of travel time across different transport alternatives and cities. This research assesses the influence of competitive travel time between car and public transit in public transit modal share for commute trips. São Paulo, New York, and Tokyo were selected to perform the analysis. A Fractional Logistic Regression in the binary form was drawn, and a competitive travel time index was calculated based on the Car/Public transit travel time ratio weighted by the number of the employed population at the origin and jobs at destinations in the absence of an Origin-Destination matrix. Findings suggest that, though car ownership was identified as the major factor, Car/Public transit travel time ratio is positively associated with the increase of public transit modal share. Furthermore, the Car/Public transit travel time ratio effect in public transit modal share consistently increases as people get increased access to cars.
Car sharing (CS) service has grown as one of the components of multimodal mobility in urban transport and it is expected to lead to a reduction in personal car ownership, thereby mitigating urban congestion and parking demand. It also provides last-mile mobility to complement public transport. Against this backdrop, a lot of studies have been conducted to make clear the effectiveness of CS services. However, most of the previous studies were surveys aimed at current CS users, and only a few studies have evaluated whether general car owners and non-owners might become members of CS services in the future.
In this study, a survey was conducted among 1000 car owners in Japan as to whether CS services would induce them to stop owning a car. At the time of the survey, the respondents’ current annual cost of owning a car was automatically displayed by calculating the amount from previous answers about their attributes. With this questionnaire method, it is presumed that respondents answered with a stronger awareness of the high cost of car ownership, so their responses might have reflected a consideration of the economic benefits of CS services. The results showed that 74% of car owners answered that they would not stop owning a car no matter how inexpensive CS services were and even if there was no risk of vehicle unavailability. The percentage was almost the same in every range of household income, and 40% said the reason was familiarity with and attachment to a car and 54% cited convenience in emergencies. These results suggest that in order to spread CS services for further reducing personal car ownership, emotional incentives are required to compensate for certain disadvantages of CS services, e.g., the lack of excitement derived from car ownership, potential non-availability in emergencies, and so on.
A survey was also conducted among 1000 non-car owners in order to analyze and compare the utility of car ownership. The results showed that the perceived utility of car ownership was 2–10 times greater for owners than for non-owners. CS service providers and urban transport policymakers should be aware of this value when designing businesses and policies. Consideration should also be given to the various objectives of CS services such as between aiming to reducing car ownership and aiming to improve urban transport for non-car owners. Further research is required to optimize CS services for efficient urban transport design.
In contrast to the traditional image of suburban communities as stable and secure enclaves surrounding a more volatile urban core, scholars have noted considerable increases in suburban poverty over the past several decades. Using data from the Panel Study of Income Dynamics (PSID) from 1999 to 2015 and a seven-category neighbourhood typology, this analysis seeks to better understand the growing economic distress of suburban areas in two stages: first by examining the degree to which low-income residents have left urban communities for suburban neighbourhoods; and second by identifying the types of low-income households that have made urban-to-suburban moves. Results show that although low-income households rapidly suburbanised during the study period, higher-income households left urban areas for suburban neighbourhoods at nearly the same rate. This finding suggests that while the overall number of economically disadvantaged residents in the suburbs has grown, population flows have had only a modest impact on the income composition of suburban neighbourhoods. Results also highlight important differences between low-income households that suburbanised during the study period and those that remained in urban communities. In particular, urban-to-suburban movers were more likely to be white, had more household resources and lived in origin neighbourhoods with fewer urban characteristics than the origin neighbourhoods of those who relocated within central-city areas.
Ridehailing quickly proliferated by geography and socioeconomic status following the 2012 introduction of UberX and Lyft, but did that proliferation extend to higher-priced, nonstandard-vehicle trips? What explains nonstandard ridehail use, who uses it, and where are they going? RideAustin trip-level data, land-use codes, ACS block groups, and parking data are analyzed using GIS and statistical software to find out. Results imply the nonstandard-vehicle, RideAustin traveler varied from the standard-vehicle traveler in several important socioeconomic ways, including access to a car, household income, race/ethnicity, age, education, and neighborhood trips per capita. About 47% of nonstandard-vehicle trips were made in neighborhoods with the highest carless household rate, and 40% of those were made in SUVs. Better tips and higher star ratings imply standard-vehicle passengers had a higher level of satisfaction compared to nonstandard-vehicle passengers. Policy recommendations are made to incentivize carpooling in these extra-capacity vehicles through subsidies.
Changing travel behaviour requires a precise understanding of the decision-making processes at work in households, in order to target public policies more effectively. The objective of this paper is to conduct a literature review to identify new research directions in the field of household demotorization, defined as the process of reducing car ownership at the individual household level. We identify three main contributions in the current literature, concerning firstly the quantification of household demotorization, secondly the influence of key events and travel socialisation on the decision to demotorize, and thirdly the role of certain transportation policies. The fourth, concluding section of the paper identifies policy implications and proposes new challenges for research in this field, which we believe deserves greater attention in the near future.
Private vehicle travel entails costs to society. However, in a world designed around the automobile, adults who lack access to a vehicle for economic reasons may experience a significant handicap due to constrained mobility and accessibility. This paper examines whether private vehicle access is associated with the quantity and quality of out-of-home activities in which low-income individuals participate. We use pooled data from multiple time use surveys drawn from the Netherlands, Canada, Spain, and the United Kingdom, and employ Cragg two-part hurdle modeling to determine whether there is an association between household vehicle access and participation in twelve out-of-home activity types. As a robustness check, we also estimate multiple discrete continuous extreme value (MDCEV) models. Further, we examine travel time by mode for those with and without vehicles. Finally, we use American Time Use Survey data and fixed-effects panel models to determine the subjective well-being that is associated with our out-of-home activity types. A lack of private vehicle access is associated with significantly less frequent out-of-home activity participation, both in the aggregate and for seven of the twelve individual activities. Moreover, the activities most likely to be foregone are generally associated with high subjective well-being, suggesting that constrained mobility comes with significant emotional costs. We find a greater “activity penalty” for rural residents and for Canadian residents without vehicle access; urbanites without vehicles in the U.K. are the only geographic group which do not exhibit an activity penalty. Finally, respondents with vehicle access spend more total time traveling, although those without private vehicles partially offset spending less time in them with higher use of alternative modes. Overall, the findings suggest that the lack of a private vehicle is deleterious for quality of life, raising troubling questions about inequity possibly arising when people are denied access to vehicles for economic reasons.
Consistent actions taken by car manufacturers to improve the quality of vehicles make them easier to use and reduce their negative impact on the environment significantly strengthen the competitiveness of personal transport. At the same time, high investments in the development of urban transport systems (especially large ones) create conditions for the mobility of all population groups, but do not significantly affect the competitiveness of public transport. At the same time, the high competitiveness of the public transport system is a sign of the successful implementation of the concept of sustainable development of the territory, making it more attractive for tourism and stimulating its development. The study of the limitations of the analysis of the competitiveness of public transport is conducted in this article along with an analysis of the factors that influence the choice of citizens in terms of mobility for their daily tasks. The article presents the results of an empirical study of relations between Moscow residents and public transport, changes in the intensity of use of personal cars in the period 2015-2020. During this period, the transport system has undergone significant positive changes, which have affected the perception of comfort and accessibility of public transport in Moscow.
We examine the fairness of congestion pricing from two perspectives. First, using data from California’s six largest urban areas, we empirically estimate the share of the population that is both economically vulnerable and likely to be impacted by freeway congestion charges. Our estimates suggest that 13 % of households would fall into this category. Second, we consider ways to mitigate burdens for this group. In particular, we compare freeway use to use of other metered network infrastructure, like electricity grids and water systems. We suggest that assistance programs from these utilities provide useful lessons for protecting low-income drivers from road prices, and argue that policymakers would be less constrained in progressively redistributing congestion toll revenue than they would be in redistributing utility revenue.
Transit use in the U.S. has been sliding since 2014, well before the onset of the COVID-19 pandemic. The largest state, California, was also losing transit riders despite substantial public investment and increased service in the pre-pandemic period. This downturn prompted concern among transit managers and planners interested in service-side interventions to reverse the decline. However, relatively little is known about changes in the demand for public transit and how shifts in demand-side factors have affected patronage. Drawing on California data from the 2009 and 2017 National Household Travel Surveys, we quantify demand-side changes as a function of two factors—changes in ridership rates of various classes of transit riders (“rate effects”) and changes in the composition of those rider classes (“composition effects”). Statewide, we find that while shifts in the population composition were in some cases associated with lower levels of ridership, the largest declines in transit patronage were associated with falling ridership rates. Specifically, those with limited automobile access and Hispanic travelers rode transit far less frequently in 2017 compared to 2009. Transit ridership rates and rider composition in the San Francisco Bay Area were relatively stable during the study period, while both rate and compositional changes in the Los Angeles area were associated with much lower levels of total ridership. Overall, our findings demonstrate the important role of demand-side factors in understanding aggregate transit use, and suggest that planners and managers may have limited policy tools at their disposal when seeking to bolster ridership levels.
Subsidies of public transit have more than doubled since the late 1980s, with a disproportionate share of funds going to rail services. These investments have important implications, including how they affect both the composition of transit users and their travel behavior. To investigate how transit users and use are changing, we use Latent Profile Analysis and data from the 2009 and 2017 National Household Travel Surveys to examine changes in transit users in the U.S. and in five major metropolitan areas. Nationwide, we find that the share of Transit Dependents grew by 17% to account for two-thirds of all transit users in 2017. These least advantaged riders were more likely over time to reside in very poor households and to be carless. There was a corresponding decline in Occasional Transit Users, for whom transit is part of a multi-modal travel profile. Higher-income, mostly car-owning Choice Transit Riders increased slightly over time but accounted for less than one in ten transit riders in 2017. Their growth was concentrated in a few large metropolitan areas where densities and land use are most transit-supportive. While increased rail transit service has shifted riders away from buses, transit’s role as a redistributive social service that provides mobility to disadvantaged travelers has grown over time. Efforts to draw more multi-modal and car-owning travelers onto transit have been less successful. As transit systems struggle to recover riders following the pandemic, transit’s waxing role of providing mobility for those without will likely become even more prominent.
In this paper we examine social and economic trends in California’s transit-friendly neighborhoods since 2000. In particular, we explore the relationship between high-propensity transit users – who we define here as members of households classified as poor, immigrant, African-American, and without private vehicles – and high-transit-propensity places – which are neighborhoods that regularly host high levels of transit service or use. As housing costs have increased dramatically in California and neighborhoods change, many planners and transit advocates reasonably worry that in transit-friendly neighborhooods, lower-propensey transit users may replace residents who tend to ride transit frequently. Such changes in residential patterns could help to explain sharp transit ridership declines in California in the 2010s ahead of much sharper pandemic-related ridership losses in 2020. Indeed, we find that California’s most transit-friendly neighborhoods have changedin ways that do not bode well for transit use. The state's shares of poor, immigrant, African American, and zero-vehicle households have all declined modestly to substantially since 2000. Collectively, these trends point to changes in California’s most transit-friendly neighborhoods that are not very, well, transit-friendly.
The COVID-19 crisis elevated the importance of private vehicles. The pandemic drove riders off public transit and spawned additional car-based activities such as drive-through testing and vaccinations and curbside pick-ups. Yet millions of low-income and non-white households do not own vehicles. This chapter draws on a unique credit panel dataset to examine automobile debt and delinquency in California. In particular, we examine whether automobile debt patterns during the pandemic differed from those during and coming out of the Great Recession (December 2007–June 2009). We also analyze the response to the COVID-19 recession across neighborhoods by income and race. Similar to the situation during the Great Recession, we find that the number of automobile loans per borrower declined. While the automobile debt burden (the ratio between total automobile debt and aggregate income) also declined, it fell far less during the pandemic than during the Great Recession. Moreover, automobile loan delinquencies spiked during the Great Recession but instead continued to drop during the pandemic. Finally, the COVID-19 crisis affected consumers differently by both race and income. Automobile debt burden rose in low-income, Latino/a, and Black neighborhoods, a pattern that preceded but continued unabated during the pandemic. The findings suggest that COVID-19 relief may have helped some families manage their automobile-related expenditures. However, other factors, such as increasing automobile prices, likely contributed to growing debt burdens, a potential source of financial distress.
Public transit offers older adults an important alternative travel mode for accessing social activity destinations. Given the growing size and socioeconomic diversity of the older population, transportation planners should consider how current urban transit systems can better meet their needs. In this study, we examine the time-varying accessibility to senior centers by public transit in each census tract of Philadelphia, Pennsylvania. Senior centers are community service and activity hubs for older adults which are typically overseen by the local area agency on aging. They offer a variety of services and social activities for older adults, including free meals, volunteering opportunities, information, and technology training. Using General Transit Feed Specification (GTFS) data and socioeconomic data from the U.S. Census Bureau, we analyze how accessibility varies across census tracts with respect to the tract's share of older adults, particularly those in vulnerable subgroups. We found that accessibility to senior centers declines from the downtown area to the suburbs but varies considerably among the vulnerable subgroups studied. We also found that when accounting for the open hours of senior centers, accessibility to senior centers is generally lower around midday (2 PM) than during peak hours (8:30 AM) on Mondays. We used the vertical equity index to examine social disparities in accessibility among older adults. We found that while SEPTA underserves neighborhoods with higher shares of older adults, tracts with higher shares of some vulnerable subgroups are better served than those with lower shares. Specifically, neighborhoods with higher shares of households that have no cars are better served than those with higher shares of African American and Hispanic older adults. These results suggest that the Southeastern Pennsylvania Transportation Authority (SEPTA) and the Philadelphia Corporation for Aging (PCA) should work together to better accommodate the diverse travel needs of older adults.
There is increasing evidence that ridehailing and other private-for-hire (PfH) services such as taxis and limousines are diverting trips from transit services. One question that arises is where and when PfH services are filling gaps in transit services and where they are competing with transit services that are publicly subsidized. Using weekday trip-level information for trips originating in or destined for the city center of Portland, OR from PfH transportation services (taxis, transportation network companies, limousines) and transit trip data collected from OpenTripPlanner, this study investigated the temporal and spatial differences in travel durations between actual PfH trips and comparable transit trips (the same origin–destination and time of day). This paper contributes to this question and to a growing body of research about the use of ridehailing and other on-demand services. Specifically, it provides a spatial and temporal analysis of the demand for PfH transportation using an actual census of trips for a given 2 week period. The comparison of trip durations of actual PfH trips to hypothetical transit trips for the same origin–destination pairs into or out of the central city gives insights for policy making around pricing and other regulatory frameworks that could be implemented in time and space.
For the sake of reducing car dependence, much can be learned from non-car owners about how everyday life can, and cannot, be organized without private car ownership. This study aims to explore carless mobility, including the role of the car, in relation to specific everyday projects and life situations. We do so through a descriptive analysis of data from the Swedish National Travel Survey 2011–2016, comparing carless mobility with that of car owners. Theoretically, our analysis builds on a constraints perspective with respect to mobility, which is rooted in time geography. We find that the constraints associated with activities and life situations seem to matter for how mobility is performed and for the feasibility of living a carless life. Managing the material flows of the household (for example, buying food and disposing of waste) is a project handled differently by non-car owners, through using nearby services and with a low degree of car use. On the other hand, our data suggest that maintaining social relations is car dependent and can potentially be more problematic for the carless. Moreover, an individual’s social network itself seems to be an important source of occasional car access. Results also indicate that the life situations of individuals may affect the mobility implications of carlessness, and the largest effect on trip frequency is found among carless retirees. From a planning perspective, and with the ambition to reduce private car use, this study identifies significant value in considering the different contexts of everyday life in which car use may or may not occur.
This paper examines urban patterns of car sharing supply and demand at neighbourhood level. By using literature on spatial inequality and unequal mobility access, we provide new insights into the urban geographies of car sharing. We draw on register data of members and vehicles of station-based car sharing for Oslo, Norway, spatially coupled with population composition, urban form and public transport accessibility ratios. We find both the demand and supply of station-based car sharing to be unevenly distributed. Some of the most profound contributors to car sharing demand include middle income levels, middle and higher levels of education, higher shares of the population working in cultural sectors, higher shares of men, higher shares of multifamily housing, and higher strengths of the public transport regime compared to that of the private car. The paper discusses the implications of these findings for sustainable urban mobility, mobility justice and urban and transport planning.
Shared mobility services, such as car-share and ride-hail programs, have become increasingly popular within the sharing economy. Shared mobility services could reduce transportation inequities among marginalized populations. However, research has yet to assess how those responsible for disseminating shared mobility services perceive these resources. To address this gap, researchers interviewed 28 community agency service providers and transportation experts. Four themes emerged about providers’ perceptions of shared mobility services. Overall, providers were unfamiliar with shared mobility services, which led to reluctance to recommend them. This study suggests that shared mobility services are under-utilized for increasing mobility among marginalized populations.
University students are regarded as a readily available market segment for public transport. In Hanoi, as elsewhere, they constitute a large portion of bus passengers. However, one portion has been quitting buses, and the reasons were so far unknown. Nor was it clear whether they planned on retuning. Through a survey of more than 800 students in seven higher education institutions, this study aimed to find the answers to these questions. The study revealed that bus ridership was determined by socio-demographic variables (year of studies, household income, employment status, motorcycle ownership), environmental variables (home-university distance), and psychological variables (convenience, bus staff behaviour, risk of sexual harassment, reliability and health, image and status). A negative disruptor such as the fear of Covid-19 infection had little effect on the decision to continue riding buses. Meanwhile, the prospect of riding 'clean and green' electric buses, which were introduced in a pilot program, was a positive disruptor that may lead a portion of students to return to public transport.
Nowadays, the issue of rural development has a central place on the agenda of policy‐makers particularly in the field of transportation. In low‐density areas, there is a lack of alternatives to car use but at the same time internal combustion vehicle are currently contested in Western societies for their impacts on global warming. As part of the solution, in the last couple of decades carsharing (CS) schemes have been growing worldwide but mainly in urban areas. This paper focuses on a recent socially oriented electric CS scheme launched in 2018 in the countryside of Le Mans, France. This program is supported by the local government whose goal is to provide new offers of transportation for those in need and also provide alternatives to cars. By studying the CS program’s first year of implementation we found a dichotomy between the social expectations of the local government and the reality of use by the population. Based on a filled‐out questionnaire and the analysis of the cars’ navigation data, we first present the profile of the early adopters. We found that they are mostly male and tend to be over 60 years old. They are also highly motorized and from the upper‐middle and upper classes. Secondly, it appears that the program is having difficulties finding regular users. In this regard, this study shows how important it is for public policy to be based on a thoughtful and coherent planning strategy as well as being led by a strong local governance.
In this paper, we examine vehicle owners’ adoption of five different types of partially automated features (PAFs); lane keeping system, backup camera (BUC), adaptive cruise control (ACC), automatic braking system (ABS), and blind spot monitoring; as well as PAF effects on vehicle miles of travel (VMT). The joint modeling of PAF adoption and VMT is achieved using both individual demographic characteristics as well as psycho-social characteristics. A Generalized Heterogeneous Data Model (GHDM) is estimated, which controls for possible self-selection effects in PAF adoption based on VMT, and thus is able to provide “true” PAF effects on VMT. Our analysis specifically indicates that ignoring this self-selection can lead to a significant underestimation of the VMT increase due to PAF adoption. The results also indicate that women and older individuals (65 years or older) appear to be more inclined to invest in assistive PAFs, because of a perception that these assistive features still leave the human driver in control. However, women are less likely than men to invest in the more active ABS PAF because of heightened safety concerns with technology. In terms of PAF effects on VMT, PAFs focusing on lateral movement assistance appear to have a smaller VMT effect than those that serve longitudinal movement assistance. The highest estimated VMT change of 2,462 miles (13.8% change) is for the case when the package of BUC, ACC, and ABS is installed for middle-aged men. The highest percentage VMT change (40%), though, is for the same package of BUC, ACC, and ABS for older women. Overall, there are considerable variations in VMT impact across demographic groupings, suggesting that a single aggregate percentage improvement in safety benefits may suffer from the well-known ecological fallacy.
Ride-hailing has redefined vehicle access and has the potential to reduce travel difficulties for transit-poor areas and people with poor access to private and public transportation resources. Due to the lack of data, current studies lack a holistic understanding of how transportation resources serve different social groups and places in the ride-hailing era in low-density areas. This study uses multiple sources of data in Austin, Texas to understand: (1) how ride-hailing usage, transit supply, and vehicle ownership distribute across neighborhoods with different densities, income, and racial and ethnic compositions; (2) who are ride-hailing users among those with and without private vehicles, and how their ride-hailing usage and attitudes towards ride-hailing versus transit differ. Our study has shown that the ride-hailing services have provided residents living in low-income, low-density neighborhoods, and neighborhoods with a majority of Hispanics with an alternative transportation mode. However, residents living in low-density and low-income neighborhoods still use ride-hailing services less frequently than those living in high-density and high-income neighborhoods. The user survey further shows that ride-hailing users without private vehicles tend to be racial and minorities or younger people with higher education. Ride-hailing services provide people with a convenient and safe transportation mode, regardless of their vehicle ownership. Our study shows the importance of ride-hailing in mitigating the social and spatial disparity and the opportunity of integrating ride-hailing and transit in transportation planning in low-density areas.
Being mobile is essential to participate in social life. However, as transport involves costs, this is a particular challenge for people on low incomes. Households with children are, especially, at an increased risk of poverty. To provide a deeper understanding of how financial poverty affects the daily travel practices of low-income families and how they cope with their limited financial resources, we conducted 16 qualitative problem-centred interviews with low-income families in Ronnenberg (Hanover Region, Germany). Although all the interviewees have to cope with limited financial resources, their daily travel practices differ. We identify four types of daily travel practices for these families: (1) car-centred, (2) car-reduced, (3) public transport oriented and (4) non-motorised. For a more detailed analysis on how poverty affects transport and participation, we use the practice theory perspective (Shove et al., 2012). Our analysis highlights that the car plays a significant role despite poverty for some families. However, other low-income families manage their daily life with public transport and non-motorised modes only. Our results show that low-income households with children have several strategies for organising and financing their daily travel practices. One strategy is direct and indirect support for travel from their social network. Furthermore, some families forgo leisure activities with entrance fees or higher travel costs.
Two broad categories of barriers to improving pedestrian and bicycle transportation are concerns about traffic safety and personal security. Gathering residents’ perceptions of these barriers can help public agencies develop effective and equitable strategies to create more sustainable transportation systems. We analyzed open-ended responses to the 2020 Milwaukee Safe and Healthy Streets survey to identify common traffic safety barriers (e.g., driver behavior such as speeding and red-light-running) and personal security barriers (e.g., undesirable street behaviors such as gun violence, robbery, and assault) to walking and bicycling. Then, we developed binary logistic models to identify perceptions of neighborhood characteristics, and individual demographic characteristics related to perceiving walking or bicycling as unsafe with respect to traffic or personal security. For walking, respondents’ traffic safety concerns were most strongly associated with perceptions of fast neighborhood traffic speeds, and personal security concerns were associated with perceptions of poor neighborhood cleanliness. For bicycling, both traffic safety concerns and personal security concerns were most strongly associated with poor neighborhood opportunities for exercise. At an individual level, living in a zero-vehicle household and having self-reported poor health were associated with rating traffic safety for both walking and bicycling as unsafe; having disabilities was associated with rating walking as unsafe. In almost every aspect of our analysis, respondents living in lower-income communities reported greater barriers to pedestrian and bicycle safety and security than residents from wealthier neighborhoods. The results emphasized the importance of both the social and physical environment for improving pedestrian and bicycle transportation.
Introduction
Safe and walkable routes to parks are associated with improved public health and increased park visits. Most studies on this topic have explored income and racial disparities in park access and safe routes to parks, and have not examined the influence of built environments on pedestrian safety around parks. To address this gap, this study explores the impacts of built environments on different scales (both street and neighborhood levels) on pedestrian safety around parks and determines whether this relationship varies by area income level.
Method
Two-level negative binomial models were used to explore the relationship between the built environment and pedestrian crashes around parks and determine how the associations varied by high- and low-income areas in Orlando, FL.
Resultsin both high
and low-income areas, increased commercial parcels and transit stops along street segments near parks were related to a higher number of pedestrian crashes, while single- and multi-family residential uses along street segments were related to fewer pedestrian crashes. Sidewalk coverage had a negative association with pedestrian-vehicle crashes, but only in low-income areas.
Conclusions
The findings demonstrate that planners should consider and examine potential traffic conflict spots near commercial uses and transit stops. Moreover, residential areas are an appropriate land use type for future park site locations, ensuring park-related pedestrian safety. Furthermore, governments should improve sidewalk coverage to reduce pedestrian-vehicle crashes in low-income areas.
Transport professional process an enormous range of information to help them accurately predict how, when and where people will use the transport system. Yet we know from a long-standing history of psychology research that people draw upon a range of mental shortcuts when making estimates about the world. Ego-centric anchoring and adjustment is a common example of a mental heuristic whereby people draw upon their own beliefs and experiences when estimating the behaviors and attitudes of others. Do transport professionals use ego-centric anchoring when estimating travel patterns? To find out we conducted a survey of transportation professionals (n = 247) who were asked to reveal their own travel patterns and residential location and to estimate the travel patterns of millennials, generation X, and baby boomers.
We find that relative to the average American, transportation professionals in the US are more likely to use alternative transport modes and live in more urban neighborhoods. Transportation professionals systematically over-estimate the use of alternative modes (and under-estimate driving) among other Americans. This effect is strongest among transportation professionals who themselves are multi-modal.
These results raise a note of caution for transport practitioners and policy-makers to be aware of one’s initial ‘anchor’ of personal experience when judging the behavior and desires of the community. For example if practitioners overestimate the proportion of Americans that are already walking, biking, and using transit, they may under-estimate the policy and institutional barriers that have led the majority of Americans to follow the car-dominated path of previous generations.
Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public transportation users and (former) car-owners. However, given their highly flexible nature and different pricing structures, previous findings on user groups and environmental impact of station-based car-sharing may not be easily transferable. Therefore, this research uses survey data to compare user groups and usage patterns of a free-floating and station-based car-sharing service both operating in the city of Basel, Switzerland. The findings suggest, that the schemes indeed attract different user groups and are also used differently. Moreover, we see, that car-sharing membership is governed by other factors than car-sharing activity.
Americans are driving less. The changes are most pronounced among Millennials, those born in the 1980s and 1990s. Much ink has been spilled debating whether these changes in travel behavior are due to changing preferences or economic circumstances. In this paper, we use eight waves of data from the Panel Study of Income Dynamics (PSID) to examine recent changes in auto ownership among US families with a particular focus on Millennials. We find that today's young adults do own fewer cars than previous generations did when they were young. However, when we control for whether young adults have become economically independent from their parents, i.e. left the nest, we find that economically independent young adults own slightly more cars than we would expect, given their low incomes and wealth. We caution planners to temper their enthusiasm about “peak car,” as this may largely be a manifestation of economic factors that could reverse in coming years.
Recent longitudinal studies of household car ownership have examined factors associated with increases and decreases in car ownership level. The contribution of this panel data analysis is to identify the predictors of different types of car ownership level change (zero to one car, one to two cars and vice versa) and demonstrate that these are quite different in nature. The study develops a large scale data set (n = 19,334), drawing on the first two waves (2009–2011) of the UK Household Longitudinal Study (UKHLS). This has enabled the generation of a comprehensive set of life event and spatial context variables. Changes to composition of households (people arriving and leaving) and to driving licence availability are the strongest predictors of car ownership level changes, followed by employment status and income changes. Households were found to be more likely to relinquish cars in association with an income reduction than they were to acquire cars in association with an income gain. This may be attributed to the economic recession of the time. The effect of having children differs according to car ownership state with it increasing the probability of acquiring a car for non-car owners and increasing the probability of relinquishing a car for two car owners. Sensitivity to spatial context is demonstrated by poorer access to public transport predicting higher probability of a non-car owning household acquiring a car and lower probability of a one-car owning household relinquishing a car. While previous panel studies have had to rely on comparatively small samples, the large scale nature of the UKHLS has provided robust and comprehensive evidence of the factors that determine different car ownership level changes.
Carsharing (or short-term auto use) organizations provide members access to a fleet of shared vehicles on an hourly basis, reducing the need for private vehicle ownership. Since 1994, 50 carsharing programs have been deployed in North America—33 are operational and 17 defunct. As of July 1, 2008, there were 14 active programs in Canada and 19 in the United States, with approximately 319,000 carsharing members sharing more than 7,500 vehicles in North America. Another six programs were planned for launching in North America by January 2009. The four largest providers in the United States and Canada support 99% and 95.2% of total membership, respectively. A 10-year retrospective examines North America's carsharing evolution from initial market entry and experimentation (1994 to mid-2002) to growth and market diversification (mid-2002 to late 2007) to commercial mainstreaming (late 2007 to present). This evolution includes increased competition, new market entrants, program consolidation, increased market diversification, capital investment, technological advancement, and greater interoperator collaboration. Ongoing growth and competition are forecast. Rising fuel costs and increased awareness of climate change likely will facilitate this expansion.
Carsharing has grown considerably in North America during the past decade and has flourished in metropolitan regions across the United States and Canada. The new transportation landscape offers urban residents an alternative to automobility, one without car ownership. As carsharing has expanded, there has been a growing demand to understand its environmental effects. This paper presents the results of a North American carsharing member survey (N = 6,281). A before-and-after analytical design is established with a focus on carsharing's effects on household vehicle holdings and the aggregate vehicle population. The results show that carsharing members reduce their vehicle holdings to a degree that is statistically significant. The average number of vehicles per household of the sample drops from 0.47 to 0.24. Most of this shift constitutes onecar households becoming carless. The average fuel economy of carsharing vehicles used most often by respondents is 10 mi/gal more efficient than the average vehicle shed by respondents. The median age of vehicles shed by carsharing households is 11 years, but the distribution covers a considerable range. An aggregate analysis suggests that carsharing has taken between 90,000 and 130,000 vehicles off the road. This equates to 9 to 13 vehicles (including shed autos and postponed auto purchases) taken off the road for each carsharing vehicle.
We evaluated the effects of on-street bicycle lanes installed prior to 2007 on different categories of crashes (total crashes, bicyclist crashes, pedestrian crashes, multiple-vehicle crashes, and injurious or fatal crashes) occurring on roadway segments and at intersections in New York City.
We used generalized estimating equation methodology to compare changes in police-reported crashes in a treatment group and a comparison group before and after installation of bicycle lanes. Our study approach allowed us to control confounding factors, such as built environment characteristics, that cannot typically be controlled when a comparison group is used.
Installation of bicycle lanes did not lead to an increase in crashes, despite the probable increase in the number of bicyclists. The most likely explanations for the lack of increase in crashes are reduced vehicular speeds and fewer conflicts between vehicles and bicyclists after installation of these lanes.
Our results indicate that characteristics of the built environment have a direct impact on crashes and that they should thus be controlled in studies evaluating traffic countermeasures such as bicycle lanes. To prevent crashes at intersections, we recommend installation of "bike boxes" and markings that indicate the path of bicycle lanes across intersections.
To assess existing research on the effects of various interventions on levels of bicycling. Interventions include infrastructure (e.g., bike lanes and parking), integration with public transport, education and marketing programs, bicycle access programs, and legal issues.
A comprehensive search of peer-reviewed and non-reviewed research identified 139 studies. Study methodologies varied considerably in type and quality, with few meeting rigorous standards. Secondary data were gathered for 14 case study cities that adopted multiple interventions.
Many studies show positive associations between specific interventions and levels of bicycling. The 14 case studies show that almost all cities adopting comprehensive packages of interventions experienced large increases in the number of bicycle trips and share of people bicycling.
Most of the evidence examined in this review supports the crucial role of public policy in encouraging bicycling. Substantial increases in bicycling require an integrated package of many different, complementary interventions, including infrastructure provision and pro-bicycle programs, supportive land use planning, and restrictions on car use.
People make tradeoffs between paid and unpaid labor, and in straight households women typically do the lion's share of unpaid labor, including household-serving travel. Nearly all of the previous research on this topic is limited to married heterosexual households with children, a surprisingly small and shrinking portion of the population. Using pooled data from the 2003–12 American Time Use Surveys, we explore how household-serving labor and travel vary across household types in the U.S. We examine the paid and unpaid labor tradeoffs made by partnered same-sex couples with and without children, and find that their division of paid and unpaid labor, as well as household-serving travel such as chauffeuring children, occupies a statistical middle ground between straight men and women. This suggests that the gendered nature of paid and unpaid work and travel is muted in the absence of a two-sex household structure, though some gendered differences persist.
This short article reflects upon the task of informing policymaking and investment decisions in times of deep uncertainty, in the face of problems that are more 'wicked' than 'tame' and in the context of ever present biases.
Background:
Many cities across the world are beginning to shift their mobility solution away from the private cars and towards more environmentally friendly and citizen-focused means. Hamburg, Oslo, Helsinki, and Madrid have recently announced their plans to become (partly) private car free cities. Other cities like Paris, Milan, Chengdu, Masdar, Dublin, Brussels, Copenhagen, Bogota, and Hyderabad have measures that aim at reducing motorized traffic including implementing car free days, investing in cycling infrastructure and pedestrianization, restricting parking spaces and considerable increases in public transport provision. Such plans and measures are particularly implemented with the declared aim of reducing greenhouse gas emissions. These reductions are also likely to benefit public health.
Aims:
We aimed to describe the plans for private car free cities and its likely effects on public health.
Methods:
We reviewed the grey and scientific literature on plans for private car free cities, restricted car use, related exposures and health.
Results:
An increasing number of cities are planning to become (partly) private car free. They mainly focus on the reduction of private car use in city centers. The likely effects of such policies are significant reductions in traffic-related air pollution, noise, and temperature in city centers. For example, up to a 40% reduction in NO2 levels has been reported on car free days. These reductions are likely to lead to a reduction in premature mortality and morbidity. Furthermore the reduction in the number of cars, and therefore a reduction in the need for parking places and road space, provides opportunities to increase green space and green networks in cities, which in turn can lead to many beneficial health effects. All these measures are likely to lead to higher levels of active mobility and physical activity which may improve public health the most and also provide more opportunities for people to interact with each other in public space. Furthermore, such initiatives, if undertaken at a sufficiently large scale can result in positive distal effects and climate change mitigation through CO2 reductions. The potential negative effects which may arise due to motorized traffic detouring around car free zone into their destinations also need further evaluation and the areas in which car free zones are introduced need to be given sufficient attention so as not to become an additional way to exacerbate socioeconomic divides. The extent and magnitude of all the above effects is still unclear and needs further research, including full chain health impact assessment modeling to quantify the potential health benefits of such schemes, and exposure and epidemiological studies to measure any changes when such interventions take place.
Conclusions:
The introduction of private car free cities is likely to have direct and indirect health benefits, but the exact magnitude and potential conflicting effects are as yet unclear. This paper has overviewed the expected health impacts, which can be useful to underpin policies to reduce car use in cities.
Income, or the lack of it, influences household transportation decisions and the ways in which individuals travel. Low-income households are less likely to own cars and more likely to travel by modes other than the automobile. Less is known, however, about the specific determinants of travel among the poor, which was the purpose of this analysis. This study used data from the 2009 National Household Travel Survey to examine the relationship between income and automobile ownership and the role of automobile ownership in explaining person miles traveled. An analysis was then made of whether these determinants varied by income group. The results indicated that low-income adults rapidly convert rising income into additional mobility at faster rates than higher-income adults. Further, automobile ownership increases person miles traveled for all adults; however, it is particularly influential in increasing the travel of low-income adults. Households accrue greater marginal benefits by moving from zero to one ve...
Strong public and political support for mass transit in the U.S. is based on lofty goals, including congestion reduction, economic development, aesthetics, sustainability, and much more. Yet, as is the case in many areas of public policy, the pursuit of multiple and broad objectives, however worthy, can diffuse efforts and fail to achieve desired results. Moreover, these goals suggest a lack of focus on the needs of transit riders themselves, particularly the poor and transit dependent. We examine this by combining data from the National Household Travel Survey, the National Transit Database, the American Public Transportation Association, and a survey we conducted of 50 U.S. transit operators. First, we find that while rail transit riders in the aggregate are approximately as wealthy as private vehicle travelers, bus patrons have far lower incomes, and this disparity is growing over time. Second, few transit agencies publicly identify serving the poor or minorities as a goal, instead focusing on objectives that appeal to more affluent riders and voters as a whole. Finally, in recent decades transit spending priorities have been slanted away from bus service and towards commuter-oriented rail services favored by the wealthier general voting public, although most members of this group rarely if ever ride transit. We contend that efforts to secure popular support for transit subsidies stifle agencies’ ability to acknowledge transit’s critical social service function and serve the needs of its core demographic. While such strategies make sense politically, underserving the poor may be poor public policy.
In previous research, we conducted a small-scale experiment in Switzerland to study the effect of a temporary use of public transportation by habitual car drivers on their commute satisfaction and mode switching. This paper reports findings from a similar experiment conducted at the Massachusetts Institute of Technology (MIT) with a larger sample, focusing on mode switching differences between the two experiments. Whereas none of the Swiss participants switched, about 30% of MIT participants switched to public transportation after the intervention (or treatment). An analysis of the underlying reasons for these differences is presented, including individual socio-demographic factors, travel attributes and institutional transportation policies, experiment context, social influences, and psychological variables. The individual and behavioral variables are compared between those who switched to public transportation and those who did not, and pre- to post-treatment changes are analyzed. Those who switched to public transportation were more predisposed to switching, were more cost-conscious, and had more favorable perceptions and attitudes towards public transportation. Those who did not switch became happier with their cars. Implications for mode switching policies employing free public transportation tickets and for institutional transportation policies are drawn.
Using a rich panel of data on welfare recipients in Alameda, Los Angeles, and San Joaquin Counties in California, this paper examined the relationship between transportation, human capital, family obstacles, socioeconomic constraints, and employment outcomes for welfare recipients. This paper reports the multinomial logit results that test the spatial mismatch hypothesis, car ownership thesis, and human capital thesis for employment outcome for welfare recipients. First, with respect to the spatial mismatch hypothesis, our work suggests that spatial proximity to jobs was not particularly important in explaining employment outcomes. Second, the private mobility measures, especially car ownership, were found to be significant predictors of employment and exiting welfare. Finally, human capital played an important role for welfare mothers who obtained a job and left the welfare system, and the number of children and their physical and mental challenges were significant barriers to economic self-sufficiency.
Many studies have shown that women work closer to home than do men, but few have probed the reasons for this persistent finding and none has done so at the metropolitan scale or considered the link between journey-to-work patterns and the occupational segregation of women. We first review the various possible reasons for women's shorter journeys to work and then examine each of these with data from the Baltimore, Maryland SMSA. We compare the work-trip distances and times of 303 employed women with those of 484 men, drawn from the 1977 Baltimore Travel Demand Data. As expected, women's work trips are significantly shorter than men's in both travel time and distance Women's lower incomes, their concentration in female-dominated occupations, and their greater reliance on the bus and auto passenger modes all help to explain their shorter work trips Male-female differences in part— versus full-time work status, occupational group, and, most surprisingly, household responsibility, did not, however, contribute to explaining observed gender differences in joumey-to-work patterns. We also found that the difference in the home and work locations of women and men could explain women's shorter work trips. A higher proportion of women than of men live and work in the central city, where journey to work distances are shorter, and there is some evidence that female-dominated employment opportunities are more uniformly distributed over the SMSA whereas male-dominated jobs are clustered in certain districts. We conclude that working women are more sensitive to distance than men are for reasons related more to their mobility than to their "dual roles" of wage earner/homemaker Also, because of women's sensitivity to commuting distance, the location of different types of employment opportunities is likely to play a role in the occupational segregation of women.
This paper presents a rare example of an experimental study investigating the effects of persuasive messages on real-life behaviour. The research aimed to explore the extent to which the low and declining use of bus services reflected overly negative public perceptions of buses and whether, if key misperceptions could be “corrected” by persuasive messages, people might change their attitudes towards bus travel and increase their use of bus services.Two controlled trials were conducted. Each comprised a before survey, delivery of marketing material to randomly selected respondents, and a follow-up survey to ascertain any changes in the attitudes and behaviour within the treated or control samples. The marketing material in the first trial was designed to “correct” common misperceptions of the bus services in the study area and to overcome perceived barriers to its use. Analysis suggests that the marketing material encouraged bus use among habitual bus users, people positively disposed towards bus and females but caused a significant decrease in bus use among males, previously infrequent users and people negatively disposed towards bus.Potential explanations for these findings were found in the Theory of Planned Behaviour (Ajzen, I. (1991). The Theory of Planned Behaviour, Organizational Behaviour and Human Decision Processes, 50, 179–211) and the Elaboration Likelihood Model (Petty, R.E., Cacioppo, J.T. (1986). The Elaboration Likelihood Model of persuasion. In: L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19). San Diego: Academic Press). A second trial was conducted using marketing material designed in light of these theories. The material, which was targeted at people who did not typically travel by bus but were apparently willing to consider doing so, acknowledged that the car is probably a first choice but highlighted occasions when the bus could be more convenient. Subsequent interviews suggested that the marketing had resulted in a significant increase in bus use by recent bus users and by males. The inclusion of a free travel offer for some recipients seemed to be particularly effective for frequent travellers and people already positively disposed towards bus.These findings are explored through a psychological lens using cognitive theories relating to individual differences in information processing, attention and memory.
The new millennium provides a good time to reflect on transportation-industry trends in some fundamental external factors that influence transportation behavior and planning response. In the public-transit industry, urban density and transit captivity have long been fundamental conditions driving transit planning and service and facility investment decisions. In light of demographic and economic changes, it is useful to revisit the issue of the importance of these factors to the transit market. Findings from a comprehensive analysis of the 1995 Nationwide Personal Transportation Survey (NPTS), which explored current transit-travel behavior, are reported. Two key findings reflect on two historical axioms in transit: (a) the extent to which density influences transit use and (b) the importance of the transit-dependent market. The research findings reiterate the significant influence that development density has on public transit mode share and bring to light some revealing data on the influence of urban-area size on transit use. The importance of transit dependency on transit use is documented, and trends in transit dependency over the past few decades are revealed. Finally, the implications of these trends for the public-transit industry are discussed.
The potential to moderate travel demand through changes in the built environment is the subject of more than 50 recent empirical studies. The majority of recent studies are summarized. Elasticities of travel demand with respect to density, diversity, design, and regional accessibility are then derived from selected studies. These elasticity values may be useful in travel forecasting and sketch planning and have already been incorporated into one sketch planning tool, the Environmental Protection Agency's Smart Growth Index model. In weighing the evidence, what can be said, with a degree of certainty, about the effects of built environments on key transportation "outcome" variables: trip frequency, trip length, mode choice, and composite measures of travel demand, vehicle miles traveled (VMT) and vehicle hours traveled (VHT)? Trip frequencies have attracted considerable academic interest of late. They appear to be primarily a function of socioeconomic characteristics of travelers and secondarily a function of the built environment. Trip lengths have received relatively little attention, which may account for the various degrees of importance attributed to the built environment in recent studies. Trip lengths are primarily a function of the built environment and secondarily a function of socioeconomic characteristics. Mode choices have received the most intensive study over the decades. Mode choices depend on both the built environment and socioeconomics (although they probably depend more on the latter). Studies of overall VMT or VHT find the built environment to be much more significant, a product of the differential trip lengths that factor into calculations of VMT and VHT.
Projections of energy use and greenhouse gas emissions for industrialized countries typically show continued growth in vehicle ownership, vehicle use and overall travel demand. This represents a continuation of trends from the 1970s through the early 2000s. This paper presents a descriptive analysis of cross-national passenger transport trends in eight industrialized countries, providing evidence to suggest that these trends may have halted. Through decomposing passenger transport energy use into activity, modal structure and modal energy intensity, we show that increases in total activity (passenger travel) have been the driving force behind increased energy use, offset somewhat by declining energy intensity. We show that total activity growth has halted relative to GDP in recent years in the eight countries examined. If these trends continue, it is possible that accelerated decline in the energy intensity of car travel; stagnation in total travel per capita; some shifts back to rail and bus modes; and at least somewhat less carbon per unit of energy could leave the absolute levels of emissions in 2020 or 2030 lower than today.
This study examines the role of car ownership in facilitating employment among recipients under the current welfare-to-work law. Because of a potential problem with simultaneity, the analysis uses predicted car ownership constructed from two instrumental variables, insurance premiums and population density for car ownership. The data come from a 1999-2000 survey of TANF recipients in the Los Angeles metropolitan area. The empirical results show a significant independent contribution of car ownership on employment. The presence of an predicted ownership is associated with a 9 percentage point increase in the odds of being employed. Moreover, the results indicate that lowering insurance premiums by $100 can increase the odds of employment by 4 percentage points. © 2002 by the Association for Public Policy Analysis and Management.
We use four waves of a longitudinal survey of current and former welfare recipients in Tennessee to examine the effects of car access on employment, weekly hours of work, and hourly wages. Contributions include a focus on car access instead of ownership, treatment of urban and rural differences, and controls for the simultaneity of car access and employment outcomes. Results indicate that car access generally increases the probability of being employed and leaving welfare. Car access also leads to more hours of work for welfare recipients with a work requirement and enables participants to find better-paying jobs.
We assess whether the positive relationship between car ownership and employment outcomes reflects a causal effect of auto ownership. We match state-level data on car insurance premiums and gas taxes to a microdata sample containing information on car-ownership and employment outcomes. In OLS regressions that control for observable covariates, we find large differences in employment rates, weekly hours worked, and hourly earnings between those with and without cars. Instrumenting for car ownership with insurance and gas taxes yields estimates of the employment and hours effects that are quite close to the OLS estimates. Concerning wages, instrumenting eliminates the positive impact of auto ownership.
Transportation scholars are challenging traditional formulations of the spatial mismatch hypothesis because previous studies have disregarded the considerable difference between travel modes. This case study of the Detroit metropolitan region uses 2000 census data and a gravity-based model of transportation accessibility to test differences in access to jobs among places and people, and provides support for recent calls for reconceptualizing spatial mismatch. It shows that even though Detroit experiences the greatest distance between African Americans and jobs of any region in the country, most central city neighborhoods offer an advantage in accessibility to jobs compared to most other places in the metropolitan region – as long as a resident has a car. Policies aimed at helping carless people gain access to automobiles may be an effective means of improving the employment outcomes of inner-city residents.
Some studies suggest that, among other obstacles to employment, welfare participants face a spatial separation from jobs and other employment-related services. Using data on welfare participants, low-wage jobs, and public transit in Lost Angeles County, this study examines the relative access that welfare participants have to employment opportunities. Our analysis shows that welfare participants access to employment varies dramatically depending on their residential location and commute mode. Many welfare participants live in job-rich neighbourhoods and are able to reach many jobs without difficulty by either car or public transit. However, other welfare participants live in job-poor neighbourhoods where a reliance on public transit significantly reduces their access to employment. In these neighbourhoods, long and unreliable commutes on public transit often severely limit their ability to find and reliably travel to and from work. Therefore given the distinctly uneven patterns of employment opportunities in metropolitan areas, policies to address the transportation needs of welfare participants should be targeted to reflect the characteristics of the neighbourhoods in which welfare participants live.