Abstract
Collecting data for traffic simulation is expensive, particularly for large simulated systems. When traditional methods are used, data are normally collected for only one day or a few days and may not represent variations in traffic demands and conditions throughout the year. Collected data usually are imperfect, and additional efforts are needed to compensate for missing and erroneous data and to resolve data inconsistencies. In recent years, agencies have started archiving data collected by intelligent transportation systems (ITS). The ITS data archives can provide cost-effective, detailed information for the development and calibration of simulation tools if procedures are used to ensure data quality and to allow optimal categorization and use of the archived ITS data. An effort to develop a series of data manipulation procedures for the use of ITS data archives in support of simulation modeling is discussed. 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 subperiods to best fit variations in demand, resolution of possible spatial inconsistencies in the data, and estimation of missing volumes. The developed procedures have been implemented as an automated tool for populating simulation models. The procedures and the developed tool can easily be adapted by other traffic agencies to interface with their ITS data archives.
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