Transportation planners and engineers need estimates of nonmotorized traffic volumes and analytical tools to plan and manage infrastructure for bicycling and walking. Direct demand models are useful, comparatively simple tools for the estimation of volumes from nonmotorized traffic counts and do not require detailed information from travel behavior inventories. However, few demand models for bicycling and walking have been validated. This paper extends the practice of nonmotorized traffic monitoring and modeling in three ways. First, procedures recommended in the FHWA Traffic Monitoring Guide are followed to present estimates of annual average daily traffic (AADT) for each segment of the urban trail networks in two major U.S. cities: Minneapolis, Minnesota, and Columbus, Ohio. Second, independent variables constructed from nationally available data sets and the local characteristics of each trail system are used to estimate and validate direct demand models for AADT. Third, to assess the potential for the general application of the models, the results of cross-city validations are presented. Our results confirm that FHWA monitoring procedures can be used to characterize the variation in traffic flows on urban trail networks. Direct demand models for each city have reasonably good fits, but the predicted traffic volumes for more than one-third of the segments exceed the actual volumes by more than 60%. Cross validation results indicate that these models cannot yet be applied as predictive tools across cities. More experimentation is needed to assess the feasibility of developing generalized direct demand models for trails and other nonmotorized transportation networks.