Counts provide the foundation for measuring nonmotorized travel along a link or a network and are also useful for monitoring trends, planning new infrastructure, and for conducting safety, health, and economic analyses. For safety analysis, they are critical in assessing the exposure to risk. Over the last decade, several automated technologies have been developed to count bicyclists and pedestrians. Despite advances in counting technology, cost and other considerations will continue to limit direct observation to small subsets of entire networks, as is the case for motorized traffic. A primary limitation with these counters is that they can only provide information about the activity that is directly on or near them but nothing about the activity on the network. The lack of widely available pedestrian count data precludes safety studies and analysis of trends, which has become critically important especially with the nationwide increase in pedestrian crashes over the last decade. The emergence of crowdsourced data such as Strava and StreetLight has allowed for the collection of large-scale datasets over broad areas of the network. While several research studies have evaluated and applied bicycle data from these datasets, no study has yet looked at pedestrian count estimates from these data sources or assessed how these compare to traditional pedestrian counts and other measures of pedestrian activity such as pedestrian actuations from traffic signals. The current study will evaluate pedestrian data estimates from the crowdsourced data sets and explore how these can be used along with traditional count data and sociodemographic data to derive count estimates.