Heterogeneous Residential Preferences Among Millennials and Members of Generation X in California: A Latent-Class Approach
Yongsung Lee, Georgia Institute of Technology (Georgia Tech)Show Abstract
Giovanni Circella, Georgia Institute of Technology (Georgia Tech)
Patricia Mokhtarian, Georgia Institute of Technology (Georgia Tech)
Subhrajit Guhathakurta, Georgia Institute of Technology (Georgia Tech)
The millennial generation, the cohort born from 1981 to 1996, lives in large cities or denser parts of metropolitan areas more than preceding generations did at the same age. To explain their residential choice, the literature points to temporary economic hardship, long-term societal changes, and changing preferences and attitudes. This study examines a less-explored but critical aspect: heterogeneous residential preferences across and within generations. In doing so, this study employs a latent-class choice model on a commuter subsample of millennials and members of Generation X (n=729) of the California Millennials Dataset, which collected a rich set of variables on various dimensions in fall 2015. Using randomly-generated unlabeled choice sets at the US Census block group level, this study finds three latent classes. The younger, pro-urban class (53%) behaves as the stereotypical millennials in popular media, preferring urban amenities; the affluent, highly-educated class (32%) appears to pursue lifestyles and high socioeconomic status over homeownership or good school districts; and the middle-class homeowner class (15%) accepts traditional family-oriented suburban lifestyles. After the examination of changing shares of the three classes by age and neighborhood type, we provide suggestions for future research and effective planning responses.
Commuting Distance and Individual Accessibility
Georgios Sarlas, ETHZ - Swiss Federal Institute of TechnologyShow Abstract
Kay W. Axhausen, Institute for Transport Planning and Systems
In the present study a methodology to construct individual-based accessibility measures is introduced. Building on the concept of specifying the distance decay function as a survival function, the transition from a data-driven approach for aggregate distance decay functions, to a model-based approach for individual-based ones is presented. Based on this, the construction of location- and person-specific accessibility measures is facilitated, allowing to take into account the various determinants of trip distance. In the case study, we make use of commuting data from Switzerland in order to estimate an ordinary least squares and a survival analysis model describing the commuting distance, in terms of generalized cost. The model estimates reaffirm the central role of accessibilities, at both ends of the trip, on the determination of commuting times. A particular focus is given on resolving the issue of multicollinearity that arises due to the inclusion of highly correlated variables. In addition, two different accessibility measures have been calculated and tested within the modeling formulations. It is concluded that a measure capable of accounting for competition at the destination bears the ability to provide valuable information about the regional function and form. Last, we exemplify the application of the proposed methodology by comparing the predicted interaction intensity for a randomly chosen person from our dataset, against the interaction intensity values as implied by an aggregate distance decay function. The results surface the existence of substantial differences that can subsequently result in large differences on the accessibility values.
Car-Deficit Households: Determinants and Implications for Household Travel
Evelyn Blumenberg, UCLA Institute of Transportation StudiesShow Abstract
Anne Brown, University of Oregon
Andrew Schouten, University of California, Los Angeles
Households with less than a one-to-one ratio between household cars and drivers (auto-deficit households) are more than twice as common as zero-vehicle households. Yet we know very little about these households and their travel behavior. In this study, therefore, we examine whether car deficits, like carlessness, are largely a result of financial constraint, or of other factors such as built environment characteristics, household structure, or household resources. We then analyze the mobility outcomes of car-deficit households compared to the severely restricted mobility of carless households and the largely uninhibited movement of fully-equipped households (households with at least one car per driver). Data from the California Household Travel Survey show that car-deficit households are different than fully-equipped households. They have different household characteristics, travel less, and are more likely to use public transit. While many auto-deficit households have incomes that presumably enable them to successfully manage with fewer cars than adults, low-income auto-deficit households are—by definition—income constrained. Our analysis suggests that low-income car-deficit households manage their travel needs by carefully negotiating the use of household vehicles. In so doing, they travel far more than carless households and use their household vehicles almost as much as low-income households with at least one car per driver. These results suggest that the mobility benefits of having at least one car per driver are more limited than we had anticipated. Results also indicate the importance of transportation and employment programs to ease the potential difficulties associated with sharing cars among household drivers.
Development of Multi-Variate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in the Xiaoshan District of Hangzhou, China
Xin Ye, Tongji UniversityShow Abstract
Jie Ma, Tongji University
This paper aims to understand the vehicle ownership of four types at the level of household, including automobile, motorcycle, electric bicycle and man-powered bicycle. This study presents a cross-sectional multivariate ordered probit (CMOP) model with composite marginal likelihood (CML) estimation approach accommodating the effects of explanatory variables and capturing the dependence among the propensity to household vehicle ownership. The sample data are obtained from the residents’ household travel survey of Xiaoshan District, Hangzhou, China in 2015. The empirical results indicate the significant effects of household sociodemographics (income, household size, home ownership and real estate price), individual sociodemographics (age, education level and license) and built environment attributes (population density and traffic zone). Interestingly, the major findings suggest that: (1) the households with higher income tend to own more autos, yet the effect is not obvious with a small value of elasticity which is similar to developed countries. (2) the household education level, which takes a positive effect on auto ownership, is a more elastic factor than income. (3) the higher population density contributes to less ownership of autos and motorcycles due to traffic congestions and parking challenges. (4) there is a large substitutive relation between auto and electric bicycle/motorcycle, and the vehicle ownership of electric bicycle/motorcycle and bicycle are mutually promoted, while motorcycle and electric-bicycle are mutually substituted.
Too Poor to Go It Alone?: The Socioeconomics of Shared Mobility in the 2010s
Brian D. Taylor, University of California, Los AngelesShow Abstract
Mark D. Garrett, UCLA Institute of Transportation Studies
Shared mobility can be serial or parallel. Serial sharing describes many new services like Lyft and Lime, while parallel shared mobility, like public transit, is more established, better for the environment, and disproportionately used by low-income travelers. This paper examines the relationships between incomes and auto access on carpooling and public transit use in the U.S. in 2017, and over time since 1995. We rely primarily on the national personal/household travel surveys of 1995, 2001, 2009, and 2017 to do this; these data are supplemented with public transit service and use data as well. We find that the strong relationships between income & auto access on one hand, and carpooling & transit use on the other, observed in earlier research persist in 2017 – and if any have grown more pronounced in recent years. Travelers in higher-income households make better than half-again as many trips by driving alone as those in the lowest-income households. On the other hand, travelers in the lowest income households (below $10,000/year) make between two and four times as many transit trips as higher-income travelers. While only 8.9 percent of U.S. households in 2017 were carless, residents in these households accounted for nearly half (46%) of all public transit trips. Despite increases in auto ownership and use in recent years, low-income and carless households remain disproportionately dependent on carpooling and public transit to get around, which is a powerful justification for public subsidy of these modes.
A Statistical Analysis of Consumers’ Perceptions Toward Automated Vehicles and Their Intended Adoption
Nikhil Menon, USF Center for Urban Transportation ResearchShow Abstract
Yu Zhang, University of South Florida
Abdul Pinjari, Indian Institute of Science
Fred Mannering, University of South Florida
Emerging automotive and transportation technologies, such as automated vehicles have created revolutionary possibilities with regard to future travel behavior. While autonomous-vehicle development continues to rapidly progress, how quickly the public will accept and adopt autonomous vehicles remains an open question. Using extensive survey data, we apply cluster analysis to better understand consumers’ perceptions toward potential benefits and concerns relating to automated vehicles with regard to factors influencing their autonomous-vehicle adoption likelihoods. Four market segments are identified which we classify as benefits-dominated, concerns dominated, uncertain, well-informed. A random parameters multinomial logit model is then estimated to identify factors influencing the probability of respondents belonging to one of these four specific market segments. Among other influences (such as socio-economic, and current travel characteristics), we find that millennials had a higher probability of belonging to the well-informed market segment, Gen-X-ers have a lower probability of belonging to the uncertain market segment, and baby boomers had a higher probability of belonging to the concerns-dominated market (relative to the golden generation). To gain a further understanding, we study the individuals’ expressed likelihood of autonomous vehicle adoption using separate random parameters ordered probit estimations for each of the four market segments. The substantial and statistically significant differences across each automated-vehicle consumer market segment underscores the potentially large impact that different consumer demographics may have on new technology adoption, and the need for targeted marketing to achieve better market-penetration outcomes with regard to autonomous vehicles.
Functional Form in Hedonic Regression: Determining the End of Significance of Transit Proximity Effects on Property Value Uplift
Robert Hibberd, University of ArizonaShow Abstract
Kristina M Currans, University of Arizona
Arthur C. Nelson, University of Arizona
This study seeks to establish policy-pertinent specification of the hedonic regression model as a measure of Property Value Uplift (PVU) in parcels surrounding transit stations, using them as a treatment cohort, through testing a variety of frequently used functional forms to empirically explore whether there exists a best-use form for modeling the effects of proximity to transit stations. This study will also extend previous literature reviews of the subject. Researchers have used many different functional forms in hedonic modeling, with widely varying results. The hypothesis for the study is that as distance to transit stations increases, direct land valuation decreases, but not necessarily along a smooth linear function Testing multiple functional forms on one data set provides a useful comparison that is hard to glean from meta-analyses with the same level of comparability of data and methodology. The study also reviews the theoretical basis for each common functional form. In addition to using the most common functional forms, this study uses distance band dummies to model proximity effects. It finds that small-distance bands can provide a reasonable fit to the range of distances from transit stations. It also finds distance bands are the most policy-relevant functional form of those reviewed, as no other form provides a series of explicit cut-off points at which to evaluate price effects of proximity to transit stations. This methodology provides improvements in land value estimations for transit system planning policies.
Shifting Gears for the Automated Vehicle: Findings from Focus Groups in the Greater Toronto and Hamilton Area
Elyse Comeau, Sajecki Planning Inc.Show Abstract
Matthias Sweet, Ryerson University
The emergence of automated vehicles (AVs) may potentially transform the ways in which individuals travel, and integrating the impacts and opportunities of AVs into travel demand forecasts and transportation planning will be important for wise decision-making. This paper presents findings from focus groups designed to explore Greater Toronto and Hamilton Area (GTHA) residents’ interest and expected behavioral responses to AVs. Results suggest that the general public is interested in AVs and eager to learn more, but that individual travel habits carry significant weight and are likely to slow the adoption of AVs. The findings from this study emphasize the planner’s responsibility to engage in consultations internally, within organizations and across departments, as well as externally, with stakeholders and members of the community to foster information gathering, dissemination, and knowledge mobilization. On-going internal and external engagements will first allow organizations to prepare and consolidate appropriate strategies for this disruptive technology, and second, will keep the public sphere informed and engaged in the implementation of AVs.
Exploring the Economic Implications of a Connected and Automated Mobility
Saana Ollila, European CommissionShow Abstract
Frank Meissner, Frame Solution Zum Havelstrand 21 14542 Werder (Havel)
María Alonso Raposo, European Commission, Joint Research Centre
Monica Grosso, European Commission, Joint Research Centre
Jette Krause, European Commission
Biagio Ciuffo, European Commission, Joint Research Centre
The transition to a Cooperative, Connected and Automated Mobility (CCAM) is likely to have substantial impacts on our society and economy. Unprecedented mobility opportunities are expected to be enabled by CCAM, potentially unveiling a range of safety, environmental and mobility/energy efficiency benefits. Deep changes in the labour market are also projected to take place, progressively making some occupations and skills less relevant, while at the same time opening up new business prospects and requiring new and more advanced skills. With Europe being a main global vehicle production player and road transport being a dominant transport mode, the full deployment of Connected and Automated Vehicle (CAV) technologies is expected to have noteworthy implications on the European economy. The economic impacts of CAVs will go far beyond the automotive industry, into sectors like insurance, logistics or health, among others. While it is clear that CAVs could offer unique opportunities for value creation, it is also essential to acknowledge that they might imply a substantial transformation of our economy and society. The present study highlights some economic challenges and opportunities linked to CCAM. It assess the potential order of magnitude of impacts in different sectors, such as automotive, electronics/software, telecommunications, transport, insurance and maintenance/repair. Moreover, it addresses structural changes in vehicle production employing a static input-output approach. Policymakers and industry players in Europe shall seize the opportunity of capturing such benefits. The findings presented in this study contribute to the ongoing debate on the type and magnitude of potential economic impacts of CCAM.
Dynamic Study of Social Diversity in Montreal
Judith Mageau-Béland, Ecole Polytechnique de MontrealShow Abstract
Catherine Morency, Ecole Polytechnique de Montreal
The segregation of social groups has been studied in the past years to show the importance of developing heterogeneous cities, increasing social diversity and social contacts. These studies have evaluated the level of diversity or segregation of population segments, by using a static perspective, typically on the basis of home location. Recent studies have attempted to develop people-based instead of place-based analyses, to account for mobility. Indeed, the development of transportation systems and mobility services also tends to develop social contacts and diversity by allowing people to move from place to place. The present research proposes a dynamic view of social diversity by accounting for the daily movements of people over an urban area. It shows, on one side, the changing composition of areas’ population using various features such as annual income, age, status and ethnicity. In the other hand, it also shows the evolving exposition people can have to different levels of social diversity through their daily movements. Some case studies are discussed: one regarding the evolution of the social factors in some selected areas of Montreal and a second on the difference in the exposition of people to changing social groups.
Coupling Up, Breaking Up, Having Kids, Graduating, and Moving: A Mobility Biography Approach to Studying Car Ownership in the United States
Nicholas J. Klein, Cornell UniversityShow Abstract
Michael Smart, Rutgers, The State University of New Jersey
What causes families to buy or give up a car in the US? We find that, coupling, breaking up, graduating college and the birth or adoption of a child have very large effects on the likelihood that families will increase or decrease their level of car ownership. Moving to or from transit-rich, dense, or walkable neighborhoods matters too, but the effects are smaller compared with life events. For transportation planners, life events represent windows of opportunity when families reevaluate their travel patterns. Finding ways to nudge families away from car ownership at these critical junctures could be an expedient way to decrease car ownership and its attendant problems, especially when combined with improving alternatives to the automobile.
Understanding the Influence of Mobility as a Service on Job Accessiblity and Transportation Equity
Fangru Wang, Georgia Institute of TechnologyShow Abstract
Catherine Ross, Georgia Institute of Technology (Georgia Tech)
Alex Karner, University of Texas, Austin
Recent innovations in passenger transportation, including ride-sourcing and automated vehicles, have attracted great interest and spawned the phenomenon known as Mobility as a Service (MaaS). Existing studies suggest that MaaS can play an important role in serving transit-dependent populations, improving the first/last mile connection of public transportation, and reducing automobile use and related environmental impacts. MaaS, provided using either conventional or automated vehicles, is likely to be a core component of our future sustainable transportation system; understanding its potential impacts has become more important than ever. Using publicly available data, this research quantifies the fine-level impact of MaaS on job accessibility and transit service equity in the Puget Sound region. The results suggest that using MaaS to serve short trips either connecting to/from transit or single modal trips can substantially elevate the existing level of job accessibility regionwide. The results of different scenarios suggest that MaaS availability and trip lengths that MaaS can be used for are the two more dominating factors influencing the extent to which job accessibility can be improved. The accessibility improvement is most significant in areas with low existing accessibility and differences are minimal across job wage categories. Policy implications and potential strategies of realizing the accessibility and equity benefits of MaaS are discussed.
Future Access to Essential Services in a Growing Smart City
Jerome Mayaud, University of British ColumbiaShow Abstract
Martino Tran, University of British Columbia
Rafael H. M. Pereira, Institute for Applied Economic Research (Ipea)
Rohan Nuttall, University of British Columbia
The concept of accessibility – the ease with which people can reach places or opportunities –lies at the heart of what makes cities livable, workable and sustainable. As urban populations shift over time, predicting the changes to accessibility demand for services becomes crucial for responsible and ‘smart’ urban planning. We investigate how projected population change could affect accessibility to essential services in the fast-growing City of Surrey in Canada. We evaluated accessibility levels to healthcare facilities and schools across Surrey’s multimodal transport network using door-to-door travel-time measurements combined with high-resolution longitudinal census data. Paying close attention to two vulnerable population groups – children and youth (0–19 years of age) and seniors (65+ years of age) – we analyzed shifts in accessibility demand from 2016 to 2022. Results show that population growth both within and outside the catchments of existing facilities will have varying implications for future accessibility demand in different areas of the city. For instance, by 2022, the city’s hospitals will be accessible to ~9,000 more people within a predefined threshold of 30 minutes by public transport. Conversely, ~27,000 more people – almost half of them seniors – will not be able to access a hospital by 2022. Since low-income and senior residents moving into poorly connected areas tend to be more reliant on public transport, accessibility equity may decline in some rural communities. Our study highlights how open-source data and code can be leveraged to analyze accessibility demand across a city, which is key for ensuring inclusive and ‘smart’ urban investment strategies.