Government stakeholders rely upon traffic information such as the weight of trucks on the roadways to provide and maintain safe and reliable highway/bridge infrastructure. Bridge weigh-in-motion (BWIM) provides an alternative to conventional static weigh stations for obtaining vehicle axle weights. Traditional BWIM algorithms are capable of predicting the axle weights of vehicles traveling at constant speed across a bridge with known influence line, but they often lose accuracy when measuring vehicles are traveling at nonconstant speed. This thesis presents a methodology to improve BWIM accuracy when measuring a vehicle traveling at nonconstant speed by transforming variable speed response data to constant speed data. A BWIM package capable of determining vehicle speed and axle spacing, calculating the influence lines of a bridge, and predicting the axle weights of a vehicle crossing the bridge is developed in MATLAB. A numerical study is performed using finite element analysis in MATLAB to evaluate the performance of the BWIM package when measuring loads traveling at constant speed and variable speed. The results of the numerical study show the speed correction is able to improve BWIM accuracy for a variable speed vehicle to nearly the accuracy level of a constant speed vehicle. A field study is also performed. A vehicle with known weight was used as a calibration vehicle to measure the influence line of a bridge on the University of Alabama campus. A different vehicle was then driven across at constant speed, then again at variable speed to generate data for various study cases. Results of the field study showed that correcting variable speed response data can significantly improve the accuracy of axle weight predictions, but more research is required to reach the accuracy level BWIM is able to achieve when measuring constant speed vehicles.