In recent years micro-simulation has become increasingly important in transportation system modeling. A potential issue is whether these models adequately represent reality and whether enough data exists with which to calibrate these models. Fortunately, over the last ten years there has been a rapid deployment of ITS technologies in most urban areas of North America. While the ITS are developed primarily for real-time traffic operations, the data are typically archived and available for traffic micro-simulation calibration. This paper presents a methodology, which uses ITS data to calibrate microsimulation models and is based on the sequential simplex algorithm. The test bed is a 23 kilometer section of Interstate 10 in Houston, Texas. Two micro-simulation models (CORSIM and TRANSIMS) were calibrated for two different demand matrices, and three different time periods (AM Peak, PM Peak, and Off-Peak). It was found for the AM Peak that the simplex algorithm had better results then either the default values or a simple, manual calibration. As the level of congestion decreased, the effectiveness of the simplex approach also decreased as compared to standard techniques
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