Described are three data quality attributes that are considered relevant to intelligent transportation system (ITS) data archiving: suspect or erroneous data, missing data, and data accuracy. Preliminary analyses of loop detector data from the TransGuide system in San Antonio were performed to identify the nature and extent of these data quality concerns in typical archived ITS data. The findings of the analyses indicated that missing data were inevitable, accounting for about one in five of all possible data records. Error detection rules were developed to screen for suspect or erroneous data, which accounted for only 1 percent of all possible data records. Baseline testing of TransGuide detector accuracy showed mixed results; one location collected traffic volumes within 5 percent of ground truth, whereas traffic volumes at another location ranged from 12 to 38 percent of ground truth. It was concluded that data quality procedures will be essential for realizing the full potential of archived ITS data.

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High-occupancy vehicle (HOV) lanes usually go through an evolution of stages in their life cycle. The typical evolution includes changes in demand levels from several modes including 2+ or 3+ carpools and vanpools, transit, and general-purpose vehicles. To ensure adequate usage, most facilities have started out with a designation of HOV2+. In some cases, over time, HOV2 volumes have exceeded the capacity of the facility, which has caused delays for transit vehicles. Therefore, there is an inevitable need for managing the hierarchy of facility users over time. A graphical tool is presented that indicates the life span of a managed HOV lane, and it can be applied to a variety of existing and planned managed HOV lane projects. The graphic was used in Colorado, Florida, and Texas in communicating the managed lane concept to transportation professionals. Further, the graphic was used to explain the historical operation of a managed HOV lane facility and the likely progression if current management policies remain in effect, based on experiences in similar facilities. Alternative management strategies can also be evaluated and compared with the graphical tool. The graphical representation of this managed HOV lane concept is anticipated to be valuable for transportation professionals in many areas (e.g., highway, tolling, and transit) in presenting and understanding operating scenarios for managed lanes over time and how they meet the goals of the facility. Applications of the life-cycle graphic to various facilities in the United States are also presented.

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.