Using intelligent transportation systems data archives for traffic simulation applications

Collecting data for traffic simulation modeling applications is expensive. Data collected using traditional methods may not represent the variations in traffic demands and conditions throughout the year and may require additional efforts to compensate for missing and erroneous data. This paper discusses a series of data manipulation procedures for the utilization of ITS data archives to support simulation modeling. These procedures allow the extraction of collected volume data from ITS data archives, automatic identification of temporal patterns in the data, automatic segmentation of daily demands into dynamically captured sub-periods, resolving possible spatial inconsistencies in the data, and estimating

missing volumes.