Transit Ridership Model Based on Geographically Weighted Regression

Modeling crash spatial heterogeneity: Random parameter versus geographically weighting

“…1 and 2 show that in some of the TAZs, the sign of the MHINC coefficient is positive. Above problem with the counterintuitive signs is not uncommon in GWR or GWPR models and has been reported in many studies (Chow et al, 2006;Hadayeghi et al, 2010;Pirdavani et al, 2013). One explanation for this problem is the existence of multicollinearity among some of the explanatory variables for some locations.…”

Modelling hedonic residential rents for land use and transport simulation while considering spatial effects

“…e GWR approach has recently found use in various applications. ere are examples in the elds of climatology , ecology (Kimsey et al 2008;Zhang and Shi 2004), education (Fotheringham et al 2001), marketing research (Mittal et al 2004), regional science (Huang and Leung 2002), political science (Calvo and Escolar 2003), and transport research (Chow et al 2006;Clark 2007;Hadayeghi et al 2003;Lloyd and Shuttleworth 2005;Nakaya 2001). In the housing eld, there are studies by Bitter et al (2007), Farber and Yeates (2006), Fotheringham et al (2002), Kestens et al (2006), Páez et al (2007), and Yu et al (2007).…”

Assessment of Models to Estimate Bus-Stop Level Transit Ridership using Spatial Modeling Methods

“…Transit ridership models were developed using a geographically weighted regression (GWR) method exploring the spatial variability in the strength of the relationship between transit use and explanatory variables that included demographics, socio-economic, land use, transit supply and quality, and pedestrian environment characteristics (Chow et al 2006;Chow et al 2010). The coefficients in a GWR model are local and vary from one location to another location (unlike in ordinary least square regression models where coefficients interpret a global relationship between a dependent variable and explanatory variables).…”

“…The coefficients in a GWR model are local and vary from one location to another location (unlike in ordinary least square regression models where coefficients interpret a global relationship between a dependent variable and explanatory variables). A comparison between the sub-regional GWR model (Chow et al 2010) and the original regional GWR model (Chow et al 2006) showed that the sub-regional GWR model performed better than the original regional GWR model in terms of model accuracy. Cervero et al (2010) recently developed a Direct Ridership Model (DRM) for Bus Rapid Transit (BRT) patronage in Southern California.…”