Video image detection system (VDS) equipment provides real-time traffic data for monitored highways directly to the traffic management center (TMC) of the Georgia Department of Transportation. However, at any given time, approximately 30 to 35% of the 1,600 camera stations (STNs) fail to work properly. The main reasons for malfunctions in the VDS system include long term road construction activity and operational limitations. Thus, providing alternative data sources for offline VDS stations and developing tools that can help detect problems with VDS stations can facilitate the successful operation of the TMC. To estimate the travel speed of non-working STNs, this research examined global positioning system (GPS) data from vehicles using the ATMS-monitored freeway system as a potential alternative measure to VDS. The goal of this study is to compare VDS speed data for the estimation of the travel speed on freeways with GPS-equipped vehicle trip data, and to assess the differences between these measurements as a potential function of traffic and roadway conditions, environmental, conditions, and driver/vehicle characteristics. The difference between GPS and VDS speeds is affected by various factors such as congestion level (expressed as level of service), onroad truck percentage, facility design (number of lanes and freeway sub-type), posted speed limit, weather, daylight, and time of day. The relationship between monitored speed difference and congestion level was particularly large and was observed to interact with most other factors. Classification and regression tree (CART) analysis results indicated that driver age was the most relevant variable in explaining variation for the southbound of freeway dataset and freeway sub-type, speed limit, driver age, and number of lane were the most influential variables for the northbound of freeway dataset. The combination of several variables had significant contribution in the reduction of the deviation for both the northbound and the southbound dataset. Although this study identifies potential relationships between speed difference and various factors, the results of the CART analysis should be considered with the driver sample size to yield statistically significant results. Expanded sampling with larger number of drivers would enrich this study results.