Exploring nonlinear built environment effects on driving with a mixed-methods approach

Recent studies have been exploring the complex nonlinear relationships between built environment attributes and driving using machine learning approaches. However, these nonlinear relationships lack causal explanations. This study applied a mixed-methods approach to data from a smaller European city, Stavanger, Norway. Our results showed that transport rationales for choosing activity locations and travel modes, along with configurations of the jobs and other facilities, provide causal explanations for the nonlinear and threshold effects of built environment attributes on people’s driving-related behavior. Distance to city center plays the most important role and its nonlinear relationship reflects the influence of the polycentric city structure of Stavanger on driving. For Stavanger and similar cities, compact development around the city center helps to rein the auto dependence. Furthermore, the thresholds of nonlinear relationships provide planning guidelines to support compact development policies.