Most route choice models are related to revealed choice behavior and are estimated by adding alternative paths to observed routes. This paper focuses on the effects of choice set composition in route choice modeling by designing an experimental analysis of actual route choice behavior of individuals driving habitually from home to work in an urban network. The numerical analysis concentrates on a qualitative perspective, by considering path sets built with different generation techniques, and a quantitative perspective, by accounting for path sets constructed with sample size reduction from each initial choice set. Comparison of prediction accuracy across different choice sets suggests that a recently developed branch and bound algorithm generates heterogeneous routes that allow for estimating models with better prediction abilities with respect to the outcomes of the drivers' actual choices. Further, comparison of route choice models across different choice set compositions indicates that nonnested structures, such as C-logit and path size logit, yield more robust parameter estimates.