Raul E. Avelar

Roadway grade is used as an input variable for several highway analysis tasks, including evaluating curve margin-of-safety, applying safety prediction models, assessing the adequacy of sight distance, and maintaining state roadlog databases. However, collecting accurate grade data is often expensive owing to the need to obtain field measurements or review as-built plan sets. Agencies would benefit from the development of a method to compute roadway grade from an automated data collection system. This paper documents a comparison of roadway grade estimations computed from global positioning system (GPS) and barometric altimeter data streams obtained during test drives on highway segments of interest. To calibrate the estimation method, the authors obtained ground-truth grade measurements collected from the field and then modeled those measures as responses in a time series model with the two data stream types as explanatory variables. The authors found that for several applications, the elevation data obtained from GPS were adequate to obtain reasonable estimates, but such estimations could be improved with a supplemental data stream from a barometer.