Connected vehicle (CV) trajectory data is able to sample information about traffic flow without infrastructure-based detection. This paper introduces a scalable method that fuses CV trajectory with signal phase and timing (SPaT) data to calibrate a well-known and widely used platoon dispersion model that describes traffic flows on links between signalized intersections. The link flow profiles are measured at multiple locations along the link with use of a virtual detection technique facilitated by the CV data. The amount of dispersion is determined by identifying the model parameters that yield the best fit between the modeled and measured profiles, where the modeled profile applies the modeled transformation using the measured upstream inflow profile as input data, which overcomes a limitation of previous studies where the inflow data could not be measured, because in real-world practice, detectors are almost never available at the required location for inflow measurements. The calibration method is demonstrated by application to a test corridor, and the results show excellent agreement between the measured and modeled flow profiles. The results also exhibit variation in the amount of dispersion that takes place by time of day.