Traffic big data can be generally divided into two categories. The first type is Eulerian observations measured with fixed roadside sensors (e.g., loop detectors, traffic cameras, radars, etc.) installed on a small set of road links. This type of data is typically owned by municipal authorities (MAs). The second type is Lagrangian observations obtained from vehicle trajectory data (e.g., real-time vehicle locations and speeds), typically possessed by mobility providers (MPs) with large fleets, such as ride-hailing companies and public transport operators. Although these data can be useful for ITS applications, they provide only an incomplete picture of transportation systems due to the partial spatial coverage of the road network (in terms of Eulerian observations) and partial penetration rate over the vehicle population (in terms of Lagrangian observations). Traffic state estimation (TSE) is an important research topic that leverages these partially observed traffic data to infer key traffic state variables (e.g., flow, density, speed) on road segments.