As railroads increase use of autonomous track geometry measurement systems, coupled with traditional measurement cars, hy-rail inspection and walking inspection, the reliability of inspection can increase. The additional data captured by autonomous inspection allows for a better understanding of track geometry perturbation growth. The distribution of accuracies associated with alternative inspection methods must be taken into account when defining the risk of a defect, which drives inspection frequency surfacing maintenance. This research project fuses these data sources to better understand track geometry perturbation growth, particularly in those areas where high impact forces and high lateral to vertical force ratios can be experienced, which may lead to derailment. The perturbation growth data is then used with inspection accuracy to be developed from published sources as well as the provided data. this in turn is used to optimize automated inspection.