Traffic simulation, a tool for recreating real-life traffic scenarios, acts as an important platform in transportation research. Considering the growing complexity of urban mobility, various large scale simulators are designed and used for research and applications. This paper proposes DRBO, a calibration framework for large scale traffic simulators. This framework combines the travel behavior adjustment with Bayesian Optimization, better exploring the structure of the simulator as well as improving its performance. By the calibration procedure, we decrease the gap between the simulator...