Much has been written about the connection between land use/urban form and transportation from the perspective of impacting automobile trip generation. This only addresses half the issue. The theoretical advances in land use/transportation relationships embodied in paradigms such as the jobs housing balance, Neo-Traditional Design (NTD) standards and Transit Oriented Development (TOD) rely very heavily on the generation of pedestrian traffic in order to realize their proposed benefits. The analysis presented here employs similar models and data sets used in Boarnet & Greenwald for the Portland, Oregon area, but applies them towards analysis of non-work walking travel. The results suggest that whatever effects land use has on affecting individual non-work walking trip generation, the impacts take place at the neighborhood level.

Travelers can be classified into two groups: choice users and captive users. Choice users select transit or automobile service when they view one option as superior, whereas captive users have only one travel option. Surprisingly, little is known about captivity effects on mode split models. This research examines the way transit service factors such as accessibility and connectivity relate to mode captivity and mode choice. Data for this investigation come from the Portland, Oregon, 1994 Household Activity and Travel Diary Survey, the Regional Land Information System for the Portland area, the U.S. Environmental Protection Agency fuel economy database, and the U.S. Department of Energy. Individual trip data were segmented into transit captive, automobile captive, and choice users based on information about private vehicle availability, transit connectivity, and distance from a transit stop. Traditional transit mode split models are compared with models that segment users into choice and captive groups. It was found that traditional models underestimate the variation in mode choice for captive users, while overestimating the attractiveness of transit for choice users. These results indicate that better transit forecasts can result if accessibility and connectivity are used to help identify captive users. Additionally, among choice transit users, differences in travel times between automobile and transit modes do little to influence mode selection, while walk access to transit has more effect than previously thought.

This qualitative study describes environmental supports and barriers to physical activity in an older adult sample drawn from low-and high-walkable neighborhoods. Thirty-seven individuals age 55 and over were recruited and answered open-ended survey questions, with a subsample invited back to partake in a semistructured interview. Content analysis identified categories and themes linking perceptions of neighborhood-environment characteristics to activity. Emerging categories and themes did not differ across neighborhood walkability, so results are presented for both groups combined. Infrastructure was the most common category identified to encourage activity, specifically, well-maintained sidewalks, bike paths or lanes, and traffic control. Other categories of land use, landscape, and aesthetics were reported. Poorly maintained or missing sidewalks, crosswalks, bike paths or lanes, and traffic safety were categories that discouraged activity. In conclusion, the information obtained is helpful in solidifying which environmental characteristics are important to measure as they relate to activity behavior in an older adult population. Keywords exercise; neighborhood design; behaviorOver the last several decades convincing evidence has accumulated documenting the fact that regular physical activity reduces the risk of premature mortality and disability from a variety of conditions including coronary heart disease, diabetes, osteoarthritis, and selected cancers (U.S. Department of Health and Human Services, 1996). Despite the known benefits of regular physical activity, older adults are among the most inactive age group in society (Centers for Disease Control and Prevention, 2004). To promote physical activity in older adults and enable them to realize its associated health benefits, it is important that their immediate surroundings be amenable to encourage such behavior. For instance, is the built, natural, and social environment supportive for older adults to be more physically active? Walking is the most prevalent form of physical activity in older adults (Crespo, Keteyian, Heath, & Sempos, 1996), so environmental characteristics might directly influence walking behavior in this population subgroup.There has been an emergence of quantitative literature examining the relationships between the built (i.e., presence of sidewalks) and social (i.e., risk of crime) environment and walking behavior in the general adult population (Frank & Engelke, 2001;Humpel, Owen, & Leslie, 2002;Owen, Leslie, Salmon, & Fotheringham, 2000;Saelens, Sallis, Black, & Chen, 2003;Sallis, Bauman, & Pratt, 1998) and also specific to older adults (Fisher, Michael, & Cleveland, 2004;King et al., 2003;Li, Fisher, Brownson, & Bosworth, 2005;Michael, Beard, Choi, Farquhar, & Carlson, 2006). This literature is beginning to document the importance of destinations within walking distance as a key motivator for walking activity, suggesting that higher densities, greater connectivity, and mixed land use support walking activity (Handy & Clifton,...

This study pools household travel and built environment data from 15 diverse US regions to produce travel models with more external validity than any to date. It uses a large number of consistently defined built environmental variables to predict five household travel outcomes – car trips, walk trips, bike trips, transit trips and vehicle miles travelled (VMT). It employs multilevel modelling to account for the dependence of households in the same region on shared regional characteristics and estimates ‘hurdle’ models to account for the excess number of zero values in the distributions of dependent variables such as household transit trips. It tests built environment variables for three different buffer widths around household locations to see which scale best explains travel behaviour. The resulting models are appropriate for post-processing outputs of conventional travel demand models, and for sketch planning applications in traffic impact analysis, climate action planning and health impact assessment.