Abstract
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.
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Goetzke, F., Andrade, P.M. (2010). Walkability as a Summary Measure in a Spatially Autoregressive Mode Choice Model: An Instrumental Variable Approach. In: Páez, A., Gallo, J., Buliung, R., Dall'erba, S. (eds) Progress in Spatial Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03326-1_11
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