Our new paper has been published on Transportation Research Part C: Emerging Technologies. We investigated the large scale of ride-sourcing vehilce fleet data for network-wide traffic speed prediction. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks.