The Virginia Department of Transportation (VDOT) has made significant investments in the traffic-monitoring infrastructure that supports intelligent transportation systems (ITS). The purpose of this infrastructure is to provide accurate, real-time information on the status of the transportation system; thus it is critical that the monitoring infrastructure provide accurate data. Although detectors are usually tested immediately after installation, it is well known that they operate in a very harsh environment and thus are susceptible to degradation in accuracy and/or complete failure. Consequently, a long-term commitment to data quality assurance is required through maintenance, data quality assessment testing, and repair/replacement. The quality of data from ITS applications is becoming increasingly important as the data are more widely used. Not only is the data used in real-time operations, but also in myriad other, often more traditional, transportation applications. The purpose of this research project was to develop a procedure that VDOT could use to assess its ITS data quality. The report includes a data quality assessment procedure that is based on theory, practice, and empirical investigation. The procedure has the following key features: benchmark data collection using temporary installation of non-intrusive detectors; data quality assessed at the lane level to pinpoint problem detectors; data quality assessed at the 1-minute interval (or minimum practical measurement interval) level to provide sufficient quantities of data in reasonable periods of time; analysis techniques including both measures and plots, which provide quantitative and visual indications of data quality. The authors recommend that VDOT begin to use the procedure on both an ad-hoc basis and in a statewide program as a means of protecting its significant investment in ITS data collection.