It has been one of the essential study methods to analyze traffic condition via the floating car data (FCD) in transportation field. The sparsity of FCD

Nevertheless

is a hindrance to researches. After analyzing the data missing features of road network

this paper put forward a filling model for the sparse FCD

which fills the random data missing based on Naive Bayes classifier and double fills the multiple data missing based on dynamic time warping. By combining the two methods mentioned above and applying them to field cases

filling the roads’ traffic flow velocity data missing can substantially increase network coverage rate of FCD and reduce the impact on collecting

launching and forecasting the traffic flow velocity data brought by the FCD missing.