How consumers acquire dynamic traveller information to adjust their travel behaviour is a key component of Intelligent Transportation Systems (ITS). The association between information acquisition/adjustments and various contextual factors can be captured in traditional statistical models, referred to as a global model. These models provide one overall set of estimated coefficients. However, associations between correlates may vary over space. Specifically, people living in different locations may have different information acquisition patterns and may respond differently to dynamic information, which cannot be fully captured in a global model. Consequently, this study uses Geographically Weighted Regression (GWR) – a locally-based model as an alternative, to answer the following questions: (1) which factors are associated with traveller information acquisition and decision adaption, (2) whether these associations are the same over the entire study region and (3) how are these associations distributed spatially? The modelling results indicate that there is spatial heterogeneity in information acquisition and use decisions, and GWR provides a more complete depiction. The models capture how correlates vary over space. The broader implication of the study is to consider spatial heterogeneity in transportation models.
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