The common understanding of "residential self-selection" generally found in research on the effects of the built environment on travel is in error in three main ways. First, scholars have generally failed to recognize that the built environment may have different effects on travel for different households. Second, controlling for residential self-selection is not necessarily relevant to the predictive questions that controlled estimates are meant to inform. Third, in controlling for preferences and sorting, the literature has failed to account for the composition of the population and its consequences for housing demand. These problems may significantly influence the validity and usefulness of the research.
Immigrants account for a majority of recent urban population growth in the United States, and for much economic growth as well. This is expected to continue for the next several decades. The foreign-born are much more likely to use transit, carpool, walk, and bicycle, particularly in their first few years of living in the United States. These trends represent challenges and opportunities for transportation and land use planners to increase the environmental sustainability of population growth, use existing transportation systems to their maximum efficiency, and support economic development. But doing so depends on anticipating the travel demands of varying immigrant groups, and those demands in turn depend on their employment and residential location choices. The authors present the most current data available on these trends, summarize research literature, and identify the major research questions needing answers to understand how to accommodate the travel demands of immigration-driven population growth.
This report analyzes how cities, transit agencies, and metropolitan planning organizations are responding to autonomous vehicles (AVs), both in terms of current testing and pilot services, as well as long-term implications of broad AV adoption. The report is based on 21 interviews with staff at cities, transportation agencies, MPOs, and select AV companies, as well as extensive document review. We found a broad spectrum of activity on the part of the public sector regarding AVs, as well as a taxonomy of motivations, which ranged from attempting to harness these vehicles to help boost transit ridership, to speeding the adoption of road pricing, increasing density, stimulating technology-sector economic development, generating revenue, and improving pedestrian safety. Agency responses to AV testing vary dramatically – from complex permitting processes and RFPs to intentional delay in developing policy so as not to deter AV activity. Publicly-led AV shuttles provide the largest opportunity for municipalities to shape AV testing, while private passenger AV testing and pilot services often provide inadequate information to cities to appraise their operations. A prospective future in which AVs make up a large share of travel has led some "early adopter" agencies to develop policies such as partnerships between public transit and AV services, changes to zoning codes to reduce parking requirements in exchange for AV drop-off and pick-up zones, and plans to tax AV passenger trips.
This paper examines the mode choice behavior of children's travel to school based on surveys conducted at a sample of schools in New Jersey. The main focus is on a variety of network design, land use, and infrastructure variables that have typically been associated with walking activity. Using a mixed logit model, it is found that good connectivity, more intense residential land use, and better sidewalk infrastructure are associated with increased walking to school. The use of a mixed logit model allows the examination of individual heterogeneity. Results indicate substantial heterogeneity in behavior associated with built environment variables.
Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.
