Autonomous vehicles(AVs) play a lead role in the future of transportation. They provide a safe travel mode by eliminating human driving error. The reduced reaction time lag associated with AVs will bring significantly more capacity to the current traffic network and help people travel more efficiently and comfortably. AVs also liberate drivers’ hands, creating more opportunities for drivers to make use of travel time. With the rapid development of machine-learning technology, it is predicted that autonomous vehicles will appear in the automobile market within two decades. This thesis integrates AVs into an existing four-step transportation model by modifying the model parameters and conducting an impact analysis on what autonomous vehicles bring to the model. Since originally there is no AV component in the model, this thesis has applied a feasible way to integrate AV behavior into the model and develop five different future scenarios to see the possible impact.