The private sector Origin-Destination (O/D) data have rapidly become a primary source in the development of travel demand models, while at the same time changes in the privacy landscape and computational algorithms are rapidly changing the nature of these data with unknown consequences to data quality and stability. This issue is further compounded by the emergence of new sources to represent trip flows, bringing into question the representativeness of such samples.  While these data are available from several vendors, the myriad approaches and technologies used to estimate O/D flows are proprietary and therefore generally impossible to assess directly.  The alternative is to assess these data against other independent data sources for internal consistency via data validation.  However, no standard guidance exists on how to conduct this validation, leaving the process to be reinvented or skipped altogether.  The task is made more difficult by differences in spatial and temporal granularity and extents as well as imputed trip characteristics provided by each vendor, meaning the guidance and reference data need to be flexible enough to evaluate a wide range of data.

The objective of this research is to identify best practices for validating proprietary third-party O/D data used to estimate travel patterns. This would involve development of guidelines on validating third-party O/D data and identification of independent non-proprietary datasets for use in validation efforts.