Transit Ridership Model Based on Geographically Weighted Regression

Abstract

This paper describes the development of a geographically weighted regression (GWR) model to explore the spatial variability in the strength of the relationship between public transit use for home-based work (HBW) trip purposes and an array of potential transit use predictors. The transit use predictors considered include demographics and socioeconomics, land use, transit supply and quality, and pedestrian environment. The best predictors identified through model estimation include two global variables (regional accessibility of employment and percentage of households with no car) and three local variables (employment density, average number of cars in households with children, and percentage of the population who are black). The models were estimated on the basis of the 2000 Census Transportation Planning Package data for Broward County, Florida. Model testing indicates the GWR model has improved accuracy in predicting transit use for HBW purposes over linear regression models. The GWR model also indicates that the effects of the independent variables on transit use vary across space. The research points to future research to explore different model structures within a geographic area.

Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China

“…Additionally, the geographically weighted regression (GWR) was constructed, and it references a family of "spatially adjusted" regressions to solve the problem of spatial autocorrelation, which is common in spatial data [10]. Chow et al [29] developed GWR models in the level of region with 2000 TAZs, and two subregions with 555 and 215 TAZs for Broward Country, Florida. Chow found that GWR models had better performance than the OLS method, and subregional GWR models had better fit than regional GWR model.…”

Geographically Weighted Regression Model: A Potential Approach For Better Management Of Urban Growth

“…GWR has become a more commonly used technique in urban studies by addressing diverse urban problems. GWR applications have widespread in the fields of ecology (Zhang & Shi, 2004;Kimsey, https://doi.org/10.15405/epsbs.2020.03.03.90 Corresponding Author: Noresah Mohd Shariff Selection and peer-review under (Brunsdon et al, 2001), education (Fotheringham et al, 2001), marketing research (Mittal, Kamakura, & Govind, 2004), regional science (Huang & Leung, 2002), political science (Calvo & Escolar, 2003), and transport research (Nakaya, 2001;Lloyd & Shuttleworth, 2005;Zhao, Chow, Li, & Liu, 2005;Chow, Zhao, Liu, Li, & Ubaka, 2006;Du & Mulley, 2006;Clark, 2007). Moreover, GWR has been applied to examine regional variations in the link between environmental variables and socio-economic indicators and to investigate geographic heterogeneity in urban and regional growths (Yu, 2006;Partridge, Rickman, Ali, & Olfert, 2008).…”

The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

“…In recent years, this model has been applied to research in the field of transportation and has provided many results [31][32][33][34]. In the research on public transportation ridership, Chow et al [35] applied a GWR model to predict bus passenger flow for home-based trips using data from Broward County, Florida. They found that the GWR model generates more accurate predictions than linear regression models.…”