The calibration of microscopic traffic simulation models has been a topic of increased attention in the transportation and traffic engineering profession. These calibration analyses have traditionally been concerned with identifying the best parameter set from a range of acceptable values. A methodology to introduce and calibrate a low parameter distribution is examined; it uses measures of central tendency and dispersion (i.e., mean and variance) to generate input parameters for car-following sensitivity factors in microscopic traffic simulation models. The approach is applied to IH-10 in Houston, Texas, with the CORSIM model and subsequently calibrated with an automated genetic algorithm methodology to examine the effectiveness of the distribution alternatives. An overview of car-following sensitivity parameters is provided, parameter distribution alternatives are outlined, and an application of the car-following distribution alternatives is discussed and compared with default distributions. The results of this analysis indicate that the distribution of car-following sensitivity parameters can be modeled in microscopic traffic simulation models and that with automated calibration methodologies such as genetic algorithms, the mean absolute error between simulated and observed traffic volume and travel time data can be minimized. The results further indicate that both lognormal and normal distribution alternatives are very effective at replicating observed conditions and representing the full distribution of car-following sensitivity factors from the calibration of the mean and variance of these parameters.