Steven R. Gehrke

Application of Geographic Perturbation Methods to Residential Locations in the Oregon Household Activity Survey: Proof of Concept

Travel demand models have advanced from zone-based methods to favor activity-based approaches that require more disaggregate data sources. Household travel surveys gather disaggregate data that may be used to inform advanced travel demand models better and also to improve the understanding of how nonmotorized travel is influenced by a households surrounding built environment. However, the release of these disaggregate data is often limited by a confidentiality pledge between the household participant and survey administrator. Concerns about the disclosure risk of survey respondents to household travel surveys must be addressed before these household-level data may be released at their disaggregate geography. In an effort to honor this confidentiality pledge and facilitate the dissemination of valuable travel survey data, this research (a) reviews geographical perturbation methods that seek to protect respondent confidentiality; (b) outlines a procedure for implementing one promising practice, referred to as the “doughnut masking technique”; and (c) demonstrates a proof of concept for this technique on 10 respondents to a household activity travel survey in the Portland, Oregon, metropolitan region. To examine the balance between limiting disclosure risk and preserving data utility, four trials were conducted and measures of household anonymity and built environment variation were analyzed for the relocated household in relation to its actual location. Results of this demonstration revealed that increases in the potential displacement distance of a geographically perturbed household generally reduced disclosure risk and also limited data utility.

Operationalizing Land Use Diversity at Varying Geographic Scales and Its Connection to Mode Choice: Evidence from Portland, Oregon

Although substantial consideration has been given to analyzing the relationship between land use diversity and travel behavior, the selection of the most suitable geographic scale for operationalizing these measures has received considerably less attention in the research. General consensus favors an examination of the complex relationship between travel behavior and such built environment measures explained at a finer spatial scale. The reasons for supporting a more disaggregate neighborhood scale include statistical advantages, such as a minimization of the modifiable areal unit problem, and more applied intentions, such as a preference for site-specific design measures seen as responsive to urban policy. Complementing this decision about how best to define the geographic extent of the built environment is the determination of which built environment measures are significantly related to travel mode choice. Of these measures, an increased diversity of land uses has often been linked with an individuals heightened likelihood for using transit, bicycling, and walking. This research advances the knowledge of which land use diversity measures best predict mode choice and explores the proper geographic scale for operationalizing these indicators. Seven diversity indexes represented at four geographic scales encompassing the origins and destinations of discretionary trips in the Portland, Oregon, metropolitan region were examined with a series of multinomial logit models. This study, which introduced several indexes previously unrecognized in transportation research, suggests common diversity measures, and the most disaggregate spatial scale may not always best represent the link between land use diversity and nonautomotive travel.

Exploring Engagement in Household Activities and Decisions on Residential Tenure and Household Type

Residential locations play an important role in the spatial distribution of household activities and travel decisions and, subsequently, in long-term forecasting models. This study examined the relationship between housing location choices, time allocation to out-of-home activities, and other socioeconomic attributes related to household life-cycle stages. A choice model of housing tenure and type was formulated and estimated to provide a methodological framework for examining the impacts of engagement in household activities and life-cycle stage. Engagement in household activities was represented by factor scores from a factor analysis on the proportion of time households spent on 14 activity types. The scores of the identified factors, in addition to other socioeconomic and travel characteristics of the household, were estimated in a nested-logit choice model of housing tenure and type. Furthermore, households were segmented into different life-cycle stages on the basis of household size and age of members. Results from this study revealed that the life-cycle stage of a household had a significant statistical impact on the tenure choice to rent relative to the choice to own a home. In relation to time allocation for household activities, the time allocated to eating, recreation, and social activities was found to have the strongest statistical significance with respect to the choice of tenure and housing type. Overall, estimation results revealed a relationship between the choice of housing tenure and type, attributes of the household, and the allocation of activity time among its members.

An Activity-Related Land Use Mix Construct and its Connection to Pedestrian Travel

Integrating a diverse set of land use types within a neighborhood is a central tenet of smart growth policy. Over a generation of urban planning research has heralded the transportation, land use, and public health benefits arising from a balanced supply of local land uses, including the improved feasibility for pedestrian travel. However, land use mixing has largely remained a transportation-land use planning goal without a conceptually valid set of environmental indicators quantifying this multifaceted spatial phenomenon. In this study, we incorporated activity-based transportation planning and landscape ecology theory within a confirmatory factor analysis framework to introduce a land use mix construct indicative of the paired landscape pattern aspects of composition and configuration. We found that our activity-related land use mix measure, and not the commonly adopted entropy-based index, predicted walk mode choice and home-based walk trip frequency when operationalized at three geographic scales.

Rail station access and housing market resilience: Case studies of Atlanta, Baltimore and Portland:

The recent United States housing market crisis resulted in a significant decline in housing market values. Yet, the extent to which urban amenities such as rail stations moderated the market impacts has not been entirely recognised. This study undertakes a repeat sales analysis to understand the impact of station proximity on housing values before, during and after the market crisis. Specifically, a housing price resilience index assesses market changes from 2002 to 2013 for single-family and multifamily homes within a quarter of a mile, half a mile, one mile and greater distances from the nearest rail station. The analysis is replicated in three cities: Atlanta, Georgia; Baltimore, Maryland; and Portland, Oregon. Although the recession had significant negative impacts on properties in each city, our study finds that access played a critical role in helping transit-orientated submarkets retain their value throughout the recession and recover value at a faster rate than homes without convenient fixed transit access.

Shared-use mobility competition: a trip-level analysis of taxi, bikeshare, and transit mode choice in Washington, DC

ABSTRACT The emergence of new shared-use mobility options such as bikeshare and ride-hailing services render the traditional dichotomy between personal vehicles and public transit somewhat irrelevant. Transportation planners and policymakers have yet to conclude whether these mobility technologies are complementing or competing against existing public transit services. The understanding of this relationship is vital given the increasing uncertainty of funding sources for transit services, but limited by the scarcity of meaningful data provided by the private ride-hailing industry. This study applies big data analytic tools on a unique travel data set to uncover the predictors motivating a half-billion transit, taxi, and bikeshare trips in rail station walksheds across Washington, DC. Study findings indicate travel cost and natural environment factors as well as land use diversity and network connectivity metrics significantly impact the likelihood for an individual to travel via taxi or bikeshare rather than rail.

Assessing the importance of housing, accessibility, and transportation characteristics on stated neighbourhood preference

ABSTRACT Beyond socioeconomic circumstance, residential location decisions are also predicated on many housing, transportation, and accessibility characteristics. Consequently, greater insight is needed on how these myriad characteristics are valued by individuals and connected to their neighbourhood preference to inform planners and decision makers concerned with urban growth patterns. Unfortunately, forecasting methods commonly lack the specificity needed to recognize how residential environment preferences influence future housing, land use, and transportation decisions. Often, these policy instruments rely exclusively on a set of observed socioeconomic characteristics to measure heterogeneity in revealed location decisions. Using stated preference data collected in Portland, Oregon, this study employed structural equation modelling techniques to examine the influence of these socioeconomic measures and latent constructs of rated single-family dwelling and non-automotive access importance on stated neighbourhood preference. Our studys findings suggest the importance placed on certain bundles of housing, transportation, and accessibility attributes, and not socioeconomic circumstance, directly affected neighbourhood preference.