Public education policies that aim to improve educational outcomes can have the effect of increasing the distance that many students must travel to attend school. In this article, we use American Time Use Survey data to examine whether longer school commutes influence time spent on important health-promoting activities. We find school commute time to be strongly inversely related to time spent sleeping, and negatively related to time spent exercising for those with long commutes. Thus, increasing journey to school distances may have troubling public health implications for teens.
Conventional wisdom within the transit industry suggests that measuring the performance of a transit project immediately after project opening may not capture all the project’s benefits, since it takes time for a project to realize its short-term ridership potential, a process commonly referred to as ridership ramp-up. Though this idea is both intuitive and appealing, especially for projects that seem to be underperforming in their initial years, there is a need for empirical analysis to determine the typical magnitude and extent of ridership ramp-up to better account for ramp-up in ridership forecasting and transit project evaluation. The purpose of this study was to meet this need by evaluating variations in ridership in the initial years after project opening for 55 rail transit projects in the United States. We applied a fixed-effects regression model to predict 1-year increases in ridership in each of the first 5 years after project opening, controlling for variation in gas prices, population, income, and unemployment. We found highly variable and statistically significant increases in ridership in the first 2 years after project opening that may be attributable to ridership ramp-up. These findings could support decisions about how to account for ridership ramp-up in forecasting and performance evaluation for rail transit projects.
There are many ways to evaluate the built environment, including measures of observable individual characteristics (such as activity density), continuous composite measures (such as the sprawl index), and categorically measured variables (such as neighborhood types). However, a systematic comparison of how well each of these three measurement types captures the influence of the built environment on travel behavior has not yet been undertaken. This lack presents a quandary for both researchers and practitioners who seek to quantify and describe the effects of the built environment on travel behavior. This paper assesses whether continuous, composite, or categorical measures provide more information and better-fitting models compared with measures of observable individual characteristics across four travel behaviors: vehicle miles traveled, walk trips, transit trips, and trip length. For each travel variable, four multivariate regression models were estimated with various measures of the built environment: activity density, sprawl index, neighborhood type, and combined sprawl index and neighborhood type. Both the sprawl index and the neighborhood-type models outperformed the activity density model. Moreover, a combined model with both the sprawl index and neighborhood types provided the best fit for all four travel behavior variables. These results suggest that both continuous and categorical composite variables provide unique and complementary information about how the built environment influences travel behavior. These findings underscore the importance of researchers’ decisions on how to represent the built environment quantitatively in models, because measurement decisions influence the understanding of how the built environment affects travel behavior.
We examine the relationship between the built environment and the travel of Millennials in the United States. We develop a neighborhood typology to characterize the built environment and transportation networks in almost every U.S. census tract, allowing us to identify possible synergistic and/or threshold effects on travel. We measure travel behavior in two ways: (1) using a multi-faceted traveler typology created using latent class analysis, and (2) by measuring the vehicle miles of travel among people in each of these traveler types. This dual approach allows us to distinguish between the built environment changes needed to encourage travel by modes other than driving, and those needed to reduce vehicle miles traveled among drivers. Using a multinomial logistic regression, we find that travel patterns are relatively stable along much of the urban-rural continuum, everything else equal. Driving was substantially lower only in “Old Urban” neighborhoods, where densities, job access, and transit service are dramatically higher than in all other neighborhood types. This finding implies that dramatic changes in the built environment—doubling or even tripling development density or transit service—may do little to get young people out of their cars when initial densities or transit services are low, as they are in most of the U.S. Conversely, reducing vehicle miles traveled among drivers appears to require more modest built form changes, a finding that offers some room for optimism among those concerned with auto dependence.
A now substantial body of literature finds that land use and urban form have a statistically significant, albeit relatively modest, effect on travel behavior. Some scholars have suggested that various built-environment characteristics influence travel more in concert than when considered in isolation. Yet few previous studies have combined built-environment measures to create holistic descriptions of the overall character of neighborhoods, and fewer still have related these neighborhoods to residents’ travel decisions. To address this gap in the literature, we develop a typology of seven distinct neighborhood types by applying factor analysis and then cluster analysis to a set of 20 variables describing built-environment characteristics for most census tracts in the United States. We then include these neighborhood types in a set of multivariate regression models to estimate the effect of neighborhood type on the travel behavior of neighborhood residents, controlling for an array of personal and household characteristics. We find relatively little variation in the number of daily trips among neighborhood types, but there is substantial neighborhood variation in both person miles of travel and mode choice. Travel by residents of one particular neighborhood type is notably distinguished from all others by a very low number of miles traveled, little solo driving, and high transit use. However, this neighborhood type is found almost exclusively in just a few very large metropolitan areas, and its replicability is uncertain.
Currently, more than 1.5 million people in the United States contribute to the maintenance of their local roads and streets through transportation utility fees charged on their monthly municipal utility bills. The fees are assessed for each property based on a particular land-use characteristic (the fee’s basis). Although their use continues to spread, transportation utility fees have faced legal challenges that generally relate to the basis a city uses to assess the fee and have limited their widespread application. This article examines the bases used by 34 cities in the United States to implement transportation utility fees and discusses how each basis relates to the success of transportation utility fees in the cities that have implemented them and in other cities that may consider adopting them.