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Traffic congestion in urban areas is posing many challenges, and a traffic flow model that accurately predicts traffic conditions can be useful in responding to them. With the limitation of infrastructure and the difficulty for real world experiment, traffic simulation is a good tool for the validation and future potential applications of the traffic flow model. This research presents a stochastic traffic flow model which uses a stochastic partial differential equation to describe the evolution of traffic flow on the highway. The stochastic model is calibrated and validated by real traffic data on a highway and is proved to have better predictive power than the deterministic model. Also, a microscopic traffic simulation is built and calibrated by the same highway data, and the validation result shows that the simulation is capable of representing the real traffic. By using traffic simulation to further explore the capability of the stochastic model, the validation demonstrated that the prediction works well at most locations, and the interpolation error may be improved by considering the influence of ramps and the change in the number of lanes.