Generational differences in automobility: Comparing America's Millennials and Gen Xers using gradient boosting decision trees

Factors influencing purchase intention of motor vehicles during the COVID-19 pandemic: An empirical study in South Africa

“…The results also highlight a variance in the behaviour of generational cohorts across different countries. The literature review states that the younger generations in the USA and United Kingdom, developed countries, generally have no appetite to own vehicles (Wang & Wang, 2021). The reality in South Africa is different because most respondents were generations Y and Z, these are the people seeking to purchase (and/or finance) vehicles even during the pandemic.…”

Socioeconomics of urban travel in the U.S.: Evidence from the 2017 NHTS

“…The final demographic factor that we examine is life-cycle classification, a factor closely related to -but not identical with -age (Garikapati et al, 2016;Wang and Wang, 2021).…”

Exploring non-linear built environment effects on the integration of free-floating bike-share and urban rail transport: A quantile regression approach

“…This application of quantile regression models also complements the recent stream of the nonlinearity of land use-travel research. Tree-based machine learning algorithms have been commonly used to identify nonlinear and threshold effects of the built environment (e.g., Cheng et al, 20020a;Ding et al, 2018;Wang and Ozbilen, 2020;Wang and Wang, 2021). These methods can offer detailed information on the effective ranges of variables of interest, thereby supporting specific policy priorities.…”