Identifying cycling-inducing neighborhoods: A latent class approach

Oliva, I. de la; Galilea, Patricia; Hurtubia, Ricardo

doi:10.1080/15568318.2018.1431822

Cited by 31 publications

(23 citation statements)

References 66 publications

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“…Existing evidence is not consistent, mainly because built-environment attributes are related to different domains of cycling behaviors ( Yang et al, 2019 ). However, those findings have provided relevant information for planning and policymaking, such as infrastructure decisions, urban planning, and public health actions ( DiGioia et al, 2017 ; Handy and Xing, 2011 ; Oliva et al, 2018 ; Pettit and Dogde, 2014 ; Rosas-Satizábal and Rodriguez-Valencia, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, most of the existing analyses addressing bicycle commuting have been focused on the built-environment factors at the origin of the trip. Only a few have considered infrastructure variables at both the origin, route and destination ( Adlakha and Parra, 2020 ; Cole-Hunter et al, 2015 ; Heinen et al, 2010 ; Oliva et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach

Higuera-Mendieta

Uriza

Cabrales

et al. 2021

Journal of Transport Geography

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There is limited evidence on the gender differences and location-specific built-environment factors associated with bicycling in Latin American cities. This study aimed to assess commuting in Bogotá by (1) analyzing the gender-specific trend of the standardized number of bicycle commuters during 2005–2017; and (2) assessing the socio-demographic, community, built-environment and natural factors associated with bicycle commuting stratified by gender. This secondary-data analysis included data from the Household Travel Surveys and Multipurpose Surveys to calculate the number of bicycle commuters per habitant from 2005 to 2017 by gender. We assessed the socio-demographic and built-environment factors fitting generalized additive models stratified by gender using the 2015 Household Travel Survey. Although both women and men increased the standardized number of bicycle commuters, male commuters show a steeper trend than women, evidencing the widening gender gap in bicycle commuting over time. Bicycle commuting was negatively associated with household motor vehicle ownership, steeper terrain slope, longer commute distance, and scarce low-stress roads at trip origin and route. Among women, the availability of bike paths at the trip destination was positively associated with bicycling, while age and being a student were negatively associated with bicycling. Among men, living in areas with the lowest socio-economic status was positively associated with bicycling, while having a driver's license and living close to bus rapid transit stations were negatively associated with bicycling. In conclusion, bicycle and transport infrastructure play different roles in commuting by bicycle by gender and trip stages (origin – route – destination).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach

Higuera-Mendieta

Uriza

Cabrales

et al. 2021

Journal of Transport Geography

View full text Add to dashboard Cite

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“…More specifically, the LCM first separates the population into different segments with a class membership model, which maximizes within-segment homogeneity and between-segment heterogeneity; it then estimates segment-specific choice models to reveal the preference heterogeneity residing in the effects of explanatory variables (Kim and Mokhtarian, 2018). The LCM allows researchers to identify various population segments with distinctive preferences, and and it has been wildly applied to assess preference heterogeneity in travel behavior studies (Eldeeb and Mohamed, 2020;Fu, 2020;Kim and Mokhtarian, 2018;Oliva et al, 2018;Shen, 2009;Vij et al, 2013;Wen et al, 2012). For example, Vij et al (2013) incorporated the influence of latent modal preferences on travel mode choice behavior by using LCM.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Preference Heterogeneity for Mobility-on-Demand Transit Service in Low-Income Communities: A Latent Segmentation Based Decision Tree Method

Zhao

Wang

Yan

et al. 2021

Preprint

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The future of public transit service is often envisioned as Mobility-on-Demand (MOD), i.e., a system that integrates fixed routes and shared on-demand shuttles. The MOD transit system has the potential to provide better transit service with higher efficiency and coverage. However, little research has focused on understanding traveler preferences for MOD transit and preference heterogeneity, especially among the disadvantaged population. This study addresses this gap by proposing a two-step method, called latent segmentation based decision tree (LSDT). This method first uses a latent class cluster analysis (LCCA) that extracts traveler profiles who have similar usage patterns for shared modes. Then, decision trees (DT) are adopted to reveal the associations between various factors with preferences for MOD transit across different clusters. We collected stated-preference data among two low-resource communities, i.e., Detroit and Ypsilanti, Michigan. The LCCA model divides the entire sample into three clusters, i.e., shared-mode users, shared-mode non-users, and transit-only users. We find that job accessibility by transit is the most important variable for all the cluster-specific DT to model the MOD transit preference, and it negatively associated with the MOD transit preference. For transit-only users, gender and car ownership are the second-important variables, but neither of them appears in the DT for the other two clusters. In particular, female transit-only users have lower preference for MOD transit, possibly due to safety concerns. The LSDT method can generate richer insights than a single DT fitted to the overall sample by better accounting for heterogeneity. The findings gained from this approach can inform better-targeted strategies to plan for MOD transit services.

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“…That prior state is often directly observed but is sometimes taken to be unobservable, as when latent class models are used to identify types of individuals or neighborhoods with distinctive propensities to cycle that do not map directly onto observed variables (e.g. Kemperman and Timmermans 2009;McDonald et al 2012;Oliva et al 2018). Moreover, the prior event or temporary state can be mental or embodied in nature, and thus include reasons, attitudes, or perceptions.…”

Section: Questioning Causalitymentioning

confidence: 99%

Uncertainty in Contextual Effects on Mobility: An Exploration of Causality

Schwanen

2018

Annals of the American Association of Geographers

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While various forms of uncertainty have been examined in studies of how geographical contexts influence mobility in recent years, this essay argues that greater attention be paid to those types that cannot be tackled automatically with better data or analysis techniques. Using cycling adoption and levels as example, it reflects on some of the uncertainties resulting from reliance in empirical research on the assumption of causality as regularity in conjunction between dependent and independent variables. It suggests that working with other understandings of causality can begin to shed light on difficult-to-detect forms of ignorance and generate more dynamic and precise insights into how contexts condition and shape behaviors and spatial practices.

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Identifying cycling-inducing neighborhoods: A latent class approach

Cited by 31 publications

References 66 publications

Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach

Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach

Assessing Preference Heterogeneity for Mobility-on-Demand Transit Service in Low-Income Communities: A Latent Segmentation Based Decision Tree Method

Uncertainty in Contextual Effects on Mobility: An Exploration of Causality

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