Study design impacts on built environment and transit use research. (January 2020)

• Record Type: Journal Article
• Authors: Aston, Laura; Currie, Graham; Kamruzzaman, Md.; Delbosc, Alexa; Teller, David
• Is Part Of: Journal of transport geography. Volume 82(2020)
• Publication Date: 2020-01
• Subjects: Study design -- Built environment -- Transit -- Public transport -- Meta-regression -- Publication bias; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2019.102625
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: The different factors examined in studies linking the built environment and transit use explain about half of the variability in findings for travel behavior. Despite many differences in the research design of these studies, it is not known if choices about study design impact theoretical consistency in results and account for some of the unexplained variance between studies. This gap exists because multiple studies must be analyzed together to explore the topic. This study aims to fill this gap, using a sample of data points and statistical models from 146 studies identified through a comprehensive database search. This paper first synthesizes the study design adopted in empirical studies of the built environment and transit use. Meta-regression is then used to identify study design aspects causing significant differences. Selective reporting bias appears to slightly exaggerate estimates for built environment Density and Accessibility. Over 40% of variability in findings for Density and Diversity was explained by study design aspects. These include whether collinearity of variables is accounted for, the specificity of the sample population and transit mode, catchment size; and the number of explanatory variables specified. Overall the average correlations for built environment and transit use are weak (<0.2). Predictions of transit ridership based on built environment factors are likely to be imprecise, so models should be carefully specified. Given the impact of study design, adherence to best practice conventions could reduce variance within studies and dispersion between studies. For ambiguous specification issues, sensitivity testing could be used to generate prediction intervals. Further investigation of factors such as transit mode and catchment size would be useful to determine if there is a theoretically plausible reason to favor certain specifications. Highlights: Study design accounts for significant differences in the prediction of transit use from built environment variables. Thirteen study design aspects were found to significantly impact the proportion of theoretically consistent results. Study design characteristics account for over 40% of variance in findings for ridership and both Density and Diversity. Selective reporting bias appears to slightly exaggerate estimates for built environment density and accessibility. More selective study design has the potential to promote comparability between studies and improve prediction reliability.