This thesis explores the concept of using sensors found in normal vehicles, also known as probe vehicles, to collect road infrastructure data. This concept was demonstrated by measuring vertical acceleration using in-vehicle sensors in order to describe road ride quality. Data collection was performed at the Virginia Smart Road using two instrumented vehicles. The gathered information was compared to road profile data collection, which is the current state-of-the-practice in ride quality assessment. Following the concept validation, the acceleration measurements were further analyzed for repeatability and effect of various independent variables (vehicle speed and type). A network-level simulation was completed using the robust set of measurements from the experiment. In addition, methodology for identifying rough sections and locations were established. Results show that under controlled testing conditions, roadway profile can accurately be estimated using probe vehicle acceleration data and may provide a more practical way to measure road smoothness. The analysis also showed that vertical acceleration data from a fleet of probe vehicles can successfully identify poorly-conditioned pavement areas. This suggests that instrumented probe vehicles might be a viable and effective way of implementing a network level roadway health monitoring program in the near future.