Global and localized neighborhood effects on public transit ridership in Baton Rouge, Louisiana
place - north america, place - urban, ridership - behaviour, ridership - commuting, planning - methods
Public transit ridership, Ecological inference method, Monte Carlo simulation, GTFS data Model, Semi-parametric geographically-weighted regression (SGWR)
This study examines what socio-demographic and spatial factors explain the variation of public transit ridership in a medium-size city in southern U.S. – Baton Rouge, Louisiana. In order to gain a sharper spatial resolution in the analysis, the ecological inference method is used to disaggregate socio-demographic data from the census block group level to the census block level. Monte Carlo simulation and transit schedule data are used to improve the estimation of travel time by private vehicle and public transit, respectively, also at the census block level. The semi-parametric geographically-weighted regression (SGWR) is used to identify, among significant variables, what are global factors and what are local factors. The results indicate that neighborhoods with higher concentrations of non-White minorities, recent immigrants and carless households have positive global effects on public transit ridership. The effects by neighborhood median income, public transit to private vehicle commuting time ratio, and accessibility to employment via public transit are localized or vary across the study area, and some of these variables even show opposite effects in specific pockets in contrast to their area-wide average effects.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Kuai, X.,& Wang, F. (2020). Global and localized neighborhood effects on public transit ridership in Baton Rouge, Louisiana. Applied Geography, Vol. 124, 102338.