Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-theart calibration by 25% on average with same input data.