Abstract
In the last few years, ordinary least squares (OLS) models have been the most used regression models for estimating trips in urban areas. However, these models present some limitations, whose the most relevant is that they ignore the geographic variations of relationships among variables. On the other hand, the explanatory power of trip generation models can be enhanced using geographically weighted regression (GWR), which is a local form of linear regression used to model spatially varying relationships.In this paper, factors affecting daily trips made by the Metro system of Madrid (Spain) are analysed through a GWR model. In order to evaluate the factors mainly influencing daily trip generation, a number of explanatory variables, including socio-economic characteristics of the population, land use, accessibility, and transportation system attributes, were considered. The analysis led to the identification of seven explanatory variables to be included in the model specification.The GWR results captured the spatial variation of the relationships among the variables across the study region. The research study attempted to identify the variable that most influenced trip generation for different parts of the city through a comparison of GWR results between various city zones.
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