Walkability as a Summary Measure in a Spatially Autoregressive Mode Choice Model: An Instrumental Variable Approach

  • Frank GoetzkeEmail author
  • Patrick M. Andrade
Part of the Advances in Spatial Science book series (ADVSPATIAL)


In recent years it has become more common to include social interactions or neighborhood effects (also called social network effects) in transportation modeling. These models are typically in the tradition of Brock and Durlauf (2001, 2002) who were among the first to propose a discrete choice model that includes social interactions and neighborhood effects. However, their approach is inherently non-spatial, while the topology of social interactions and neighborhood effects can be best captured spatially (Leenders 2002; Páez et al. 2008a). Therefore, some of the latest articles in transportation modeling have moved towards explicitly incorporating the spatially autoregressive structure of social network effects into their models (e.g. Dugundi and Walker 2005; Páez and Scott 2007; Goetzke 2008). This new direction in transportation research is not all that surprising, given the success of spatial econometrics as an emerging modeling method across social science disciplines. The econometric strategy to implement an independent variable representing social interactions and neighborhood effects, as proposed by Brock and Durlauf (2001, 2002), is to use the group mean of the observed dependent choice variable, as defined by social interactions and neighborhood effects. This approach can be spatially extended if the group mean is based on spatial relations, as in traffic analysis zones (Dugundi and Walker 2005), a spatial weight matrix (Goetzke 2008), or a matrix based on personal relations (Páez and Scott 2007). Therefore, choices could be modeled as a function of the typical choice determinants in travel behavior analysis (e.g. personal, household, trip and mode characteristics), or as choices of either a non-spatial group or spatial neighbors. Empirical work dealing with mode choice decisions by Dugundi and Walker (2005), and Goetzke (2008) give evidence that the mode choice decisions of spatial neighbors are indeed associated with the mode choice decision of the individual.


Mode Choice Neighborhood Effect Discrete Choice Model Transportation Modeling Contextual Interaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Urban and Public AffairsUniversity of LouisvilleLouisvilleUSA

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