It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are mainly used to estimate the real-time traffic conditions on road segments, and has done little for turn delay estimation. However, turn delays on road intersections contribute significantly to the overall travel time on road networks in modem cities. In this paper, we present a technical framework to calculate the turn delays on road networks with float car data. First, the original floating car data collected with GPS equipped taxies was cleaned and matched to a street map with a distributed system based on Hadoop and MongoDB. Secondly, the refined trajectory data set was distributed among 96 time intervals （from 0：00 to 23： 59）. All of the intersections where the trajectories passed were connected with the trajectory segments, and constituted an experiment sample, while the intersections on arterial streets were specially selected to form another experiment sample. Thirdly, a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm argued is not only statistically fitted the real traffic conditions, but also is insensitive to data sparseness and missing data problems, which currently are almost inevitable with the widely used floating car data collecting technology. We adopted the floating car data collected from March to June in Beijing city in 2011, which contains more than 2.6 million trajectories generated from about 20000 GPS-equipped taxicabs and accounts for about 600 GB in data volume. The result shows the principal curve based algorithm we presented takes precedence over traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy （about 10%-15% higher in RMSE）, as well as reflecting the changing trend of traffic congestion. With the estimation result for the travel delay at intersec
