Travel modelers commonly use passive transportation-related data, such as cellular or GPS origin-destination matrices, to calibrate or validate their planning models. The passive data are generally used, in this case, to expand and adjust behavioral models estimated from a small-sample local household survey. Recently, there has been interest in deriving synthetic records of individual travel directly from the passive data, with the support of other datasets. Kressner (2017) previously developed a method to build synthetic daily activity and travel patterns for a complete population. In this method, simulated individuals used the 2009 NHTS as a basis for tour patterns while passive origin-destination matrices spatially locate the simulated tours within a specific region. In this work, we update our tour patterns to the 2017 NHTS and comment on observed differences in the resulting simulated travel patterns. Specifically, we consider the temporal distribution of weekday trips. We also examine the consequences of these differences in the synthesized daily schedules and in their traffic assignment. The results show that the NHTS has a similarly high proportion of mid-day, off-peak travel in both 2009 and 2017, and that this proportion is higher than other comparable local household surveys. The results also show that the change from the 2009 to the 2017 dataset does not substantively affect the simulated demand or assignment in a data-driven travel model using NHTS data.