To date, the primary sources of data for travel modeling have been comprehensive household travel surveys, which are collected at high cost and yet tend to have measurement error and nonresponse bias. There has been interest in using passive data for travel modeling, but existing trip- and tour-based models require a direct link between demographics and trip-making behavior that is uncommon in passive data. This project demonstrates an attempt to overcome these limitations by building an agile tour-based model with passive data using an innovative person-based discrete event simulation framework. The simulation-based model is compared with a modern trip-based model recently developed for the North Carolina Department of Transportation (NCDOT) and the French Broad River Metropolitan Planning Organization (FBRMPO) covering the Asheville region in North Carolina. Demand trip tables from both models are fed into the region’s static assignment model and validated against traffic counts. The assignment results are similar in terms of average link error against traffic count data; this is the case even though the simulation-based passive data model was neither calibrated to local conditions nor adjusted with scaling factors or shadow prices. A discussion of costs, development time, complexity, and usability between NCDOT’s recent aggregate trip-based model and the new tour-based model constructed from passive data are provided. We believe the new modeling approach developed through this research will be a good fit for small and medium-sized communities, as it permits tour-based models to be developed with substantially less investment in time, money, and data.