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■ Abstract The thesis of this article is that multilevel interventions based on ecological models and targeting individuals, social environments, physical environments, and policies must be implemented to achieve population change in physical activity. A model is proposed that identifies potential environmental and policy influences on four domains of active living: recreation, transport, occupation, and household. Multilevel research and interventions require multiple disciplines to combine concepts and methods to create new transdisciplinary approaches. The contributions being made by a broad range of disciplines are summarized. Research to date supports a conclusion that there are multiple levels of influence on physical activity, and the active living domains are associated with different environmental variables. Continued research is needed to provide detailed findings that can inform improved designs of communities, transportation systems, and recreation facilities. Collaborations with policy researchers may improve the likelihood of translating research findings into changes in environments, policies, and practices.

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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.

In this study, we present exploratory evidence of how "ridesourcing" services (app-based, ondemand ride services like Uber and Lyft) are used in San Francisco. We explore who uses ridesourcing and for what reasons, how the ridesourcing market compares to that of traditional taxis, and how ridesourcing impacts the use of public transit and overall vehicle travel. In spring 2014, 380 completed intercept surveys were collected from three ridesourcing "hot spots" in San Francisco. We compare survey results with matched-pair taxi trip data and results of a previous taxi user survey. We also compare travel times for ridesourcing and taxis with those for public transit. The findings indicate that, despite many similarities, taxis and ridesourcing differ in user characteristics, wait times, and trips served. While ridesourcing replaces taxi trips, at least half of ridesourcing trips replaced modes other than taxi, including public transit and driving. Impacts on overall vehicle travel are unclear. We conclude with suggestions for future research.

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Car-dependent cities, some claim, contribute to obesity by discouraging walking and bicycling. This paper uses household activity data from the San Francisco region to study the links between urban environments and non-motorized travel. Factor analysis is used to represent the urban design and land-use diversity dimensions of built environments. Combining factor scores with control variables, like steep terrain, which gauge impediments to walking and cycling, discrete-choice models are estimated. Builtenvironment factors exerted far weaker, though not inconsequential, influences on walking and cycling than control variables. Stronger evidence on the importance of urban landscapes in shaping foot and bicycle travel is needed if the urban planning and public health professions are to forge an effective alliance against car-dependent sprawl.

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