Emergency response is affected by both static and dynamic variables within a street network. Static variables are constant and do not change while dynamic variables change over time. When variables are input into emergency response, modeling the prediction of travel time will be more accurate which results in lives saved. This study focuses on modeling both static and dynamic variables within a street network model. This model incorporates the static variables of turn penalties, slope adjustment, and the dynamic variable of weather impact. The variables have been tested both individually and in conjunction with one another. The goal of this study is to develop a GIS model which predicts travel time for emergency vehicles with multiple variables included. The output travel times from this model were compared with actual travel time records in order to determine if the included variables have any impact on emergency response time. The model was developed using a combination of Python scripting and the Network Analyst extension in ArcGIS Desktop. Weather data in this study was downloaded from the National Weather Service. Other GIS data was provided by The Omega Group and a variety of other sources. Analysis of model results was accomplished with Correlation Analysis within Microsoft Excel. This statistical method determines how well the model output correlates with the actual travel times. The time recording methods showed no discernible impact on the model results. The static variables of turn penalties and slope adjustment showed only minor changes in accuracy, mostly below one percent. The dynamic weather variable improved the model accuracy significantly in all three study areas. The conclusion is that weather conditions effect emergency response time significantly. Turn penalties and slope adjustment do not have a large affect emergency response time.