Tracking multiple vehicles in wide area traffic scenes is challenging due to high target density, severe similar target ambiguity, and low frame rate. In this paper, we propose a novel spatio-temporal context model, named maximum consistency context (MCC), to leverage the discriminative power and robustness in the scenario. For a candidate association, its MCC is defined as the most consistent association in its neighborhood. Such a maximum selection picks the reliable neighborhood context information while filtering out noisy distraction. We tested the proposed context modeling on multi-target tracking using three challenging wide area motion sequences. Both quantitative and qualitative results show clearly the effectiveness of MCC, in comparison with algorithms that use no context and standard spatial context respectively.