Scales, Segments, and Neighborhoods of Change: An Investigation of Active Mobility Adoption

Biehl, Alec Michel

doi:https://doi.org/10.21985/n2-q3ab-bf70

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Scales, Segments, and Neighborhoods of Change: An Investigation of Active Mobility Adoption

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The purpose of this dissertation is to investigate the adoption of active mobility through the lens of three fundamental concepts: scale, segment, and neighborhood. Scale refers to both the aggregation of geospatial data and the measurement of latent constructs through behavioral survey instruments. Segment refers to the various clustering approaches that are used to unveil patterns in the data; these approaches range from a priori methods, such as the stage-of-change framework derived from the Transtheoretical Model, to post hoc methods, which are either data-driven (e.g. mean-shift, agglomerative) or model-based (i.e. latent class analysis) in nature. Neighborhood refers to not only the spaces and places that constitute an urban tapestry, but the areas contained within specified thresholds that denote susceptibility to movement from one segment to another. This S-S-N conceptualization provides a novel parameterization of travel behavior change research that illuminates psychological and socio-cultural dimensions often overlooked in the literature. The emphasis on active mobility in this dissertation arises from the growing interest in the potential community-oriented and quality-of-life benefits offered by walking and cycling activity, often considered as aspects of a transit-oriented transportation system. Moreover, the burgeoning bike share phenomenon represents a critical nexus of pro-environmental and pro-social decision-making regarding travel, as leading agents in the sharing economy attempt to bolster collaborative consumption and sustainable living. Questions remain, however, about the readiness or willingness of individuals and communities to adopt these three modes, in turn giving rise to concerns about the extent to which transport policies and infrastructure investments address fundamental questions of equity. To this end, the author utilizes three distinct data sources to investigate walking, cycling, and bike-sharing uptake: (a) focus groups investigating built environment, well-being, and cultural considerations of mobility; (b) an online behavioral survey that implements an innovative stage-of-change framework and collects information on important socio-demographic, geographic, behavioral, and attitudinal variables; and (c) Divvy bike share system usage combined with data from the 2016 American Community Survey, EPA Smart Database, and Chicago Data Portal. Five analyses constitute the backbone of this dissertation. First, the utility of stage-based behavior change theories is demonstrated through the construction of (a) continuation ratio ordinal logit models for walking and cycling adoption, and (b) a nested logit model for bike share adoption. This distinction is made due to the different stage assignment algorithms employed in the survey yet results in both cases point to the need to better understand identity construction, normative influence, and multimodalism as agents of travel behavior change. Second, the average daily bicyclist (ADB) of Chicago’s Divvy bike share system is examined using a generalized linear model with station- and community area-level covariates. Elasticity calculations, influence and cluster analyses illuminate the socio-spatial patterns underpinning system usage in a manner that undermines the notion that accessibility and equity are equivalent notions. Third, taking a broader perspective on active travel, the development of (a) multiple-indicators multiple-causes (MIMIC) structural equation and (b) latent class models confirms the status of stage-of-change frameworks as valuable emerging tools for transportation researchers and practitioners. Importantly, however, these market segmentation strategies should be complementary to more conventional approaches in transport policy. Fourth, and in a similar vein, the application of text mining algorithms to focus group research conducted in two Chicago community areas reveals much-needed guidance for city-scale policy interventions. Fifth and finally, the integration of (a) graph-based clustering approaches with (b) support vector machines explores how the pseudo-longitudinal nature of stage-of-change data could boost the effectiveness of tailored travel behavior change campaigns through the generation and testing of a new susceptibility metric.

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