Southern California is prone to wildfire events that spark evacuations of communities in the Wildland-Urban Interface. In December 2017, the Lilac Wildfire ignited in northern San Diego County, forcing 10,000 residents to evacuate. Highly developed regions such as Southern California have a number of transportation data sources to draw from that can support emergency managers decision making processes. Up to date traffic sensors such as those found on the majority of California’s highways can inform emergency managers on current traffic densities, flows and speeds. Yet, in many wildfire prone regions of the United States, this is not the case. Despite this data shortfall, many regions do have robust cellular networks that inherently produce substantial amounts of location data. The location data produced by cellphone users can be used to predict vehicular densities on evacuation routes. This thesis will examine how cellular data can be used to predict vehicular densities on evacuation routes. Correction factors were developed to adjust for the overestimation of users on roadways by cellular networks. Extrapolation factors were also developed for estimation of the number of cellular users based on a single cellphone “counts” data point. Finally, a simple equation was developed to aid in the prediction of vehicular densities on evacuation networks. This methodology may prove useful to transportation planners and emergency managers in planning evacuations in areas not served by a network of traffic sensors.