The real-time data feeds from these two large-scale platforms present unprecedented opportunities for us to study vehicular networking at extraly-large scale in a real-time fashion. For example, 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, we start a project called 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. In particular, we design (i) convex multi-view learning to integrate multi-source data by quantifying biases of data sources, and (ii) context-aware tensor decomposition to infer incomplete multi-source data by extracting real-world contexts. More importantly, we implement and evaluate MultiCalib with the above nationwide vehicle networks to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China.