Incomplete Archived Data of Intelligent Transportation Systems for Calculation of Annual Average Traffic Statistics What Level of Missing Data Causes Problems? Academic Article uri icon


  • Some transportation planning and traffic operationsintelligent transportation system (ITS) groups are cooperating on data-sharing initiatives, including the use of archived ITS data for planning statistics. However, conventional procedures for the calculation of annual average traffic statistics need to be updated to accommodate the incomplete nature of archived ITS data. The effects of various missing data patterns on several existing and modified annual average statistic calculation procedures were tested. Five locations were chosen for the study: two urban Interstate highways with significant commuter traffic, one urban parkway with commuter traffic and recreational trips, and two rural roads with pronounced seasonal patterns. A complete year of data was obtained for each study location; then missing data patterns (as identified from an empirical study) were simulated by randomly or systematically removing data. At urban locations in which commuter traffic dampens seasonality patterns, a significant amount of missing data (up to 8 months of consecutive missing data) can be tolerated with little to no effect on annual average traffic statistics. All calculation procedures provided similar results at the urban locations. For the two rural locations, 1 month of missing data still resulted in tolerable error for most procedures, whereas 2 months of missing data were close to or exceeded tolerable error levels for most procedures. Modifications of conventional calculation procedures (that account for small gaps in data on a daily basis) are better suited to archived ITS data.

published proceedings


author list (cited authors)

  • Turner, S., & Park, E. S.

citation count

  • 4

complete list of authors

  • Turner, Shawn||Park, Eun Sug

publication date

  • January 2008