The ability to infer the traffic status across multiple cameras allows the extended use of existing vision-based surveillance systems to global traffic monitoring. In this paper, we propose an efficient algorithm to probabilistically model the dynamic traffic flow between non-overlapping FOVs. By assuming the transition time of object moving across cameras follows a global model and consecutively estimate the model parameters, we may infer the time-varying traffic status in the unseen region. In principle, the parameters of the transition time model can be estimated if the object correspondence between non-overlapping FOVs is known. However, object correspondence itself is still an unsolved problem in computer vision. In this paper, we model object correspondence and the parameters estimation as a unified problem in a proposed Expectation-Maximization (EM) based framework. By treating object correspondence as a latent random variable, the proposed framework can iteratively search for the optimal object correspondence and model parameters. Experimental results on real data show the accuracy of dynamic model estimation and the beneficial inference of the traffic status.