Today, numerous alternative modes of mobility are emerging to provide a solution to the problems created by the automobile. This research envisions a future where transportation in urban areas will be dominated by small personal mobility devices (PMDs) instead of automobiles. This Intelligent Mobility System (IMS) would be a car-free zone where people travel by a shared-system of PMDs providing levels of mobility greater than walking but less than a car. This research effort focuses on the operational aspects of this future system by studying PMD performance characteristics as inputs for a computer simulation model of an IMS environment. Therefore, the primary objective of this research is to evaluate the operations of PMDs that are currently used in a variety of settings. GPS recorders are used to log speed and location data each second of pedestrian, bicycle, Segway, and electric cart trips. Segway speed and acceleration are analyzed using three factors, sidewalk width, surface quality, and pedestrian density to study their effect on Segway speed. Pedestrians have the lowest mean speed and the most narrow speed distribution. Segways, bicycles and electric carts have increasingly faster mean speeds and wider speed distributions, respectively. Segways and bicycles were found to have similar acceleration distributions. Segways seem to provide a level of speed and mobility between that of pedestrians and cyclists, meaning that Segways might capture new users by providing a level of mobility and convenience previously unseen. Narrow sidewalk widths, poor sidewalk quality, and heavy pedestrian density all decreased Segway speeds. The researchers suspect that surface quality is likely an independent constraint for Segway speed and that sidewalk width and pedestrian density interact to limit Segway speeds under certain conditions. This research concludes that these external factors may affect PMD speed and should be considered when analyzing PMD mobility, especially in an IMS setting.