  As an important information for traffic condition evaluation, trip planning,
transportation management, etc., average travel speed for a road means the
average speed of vehicles travelling through this road in a given time
duration. Traditional ways for collecting travel-speed oriented traffic data
always depend on dedicated sensors and supporting infrastructures, and are
therefore financial costly. Differently, vehicular crowdsensing as an
infrastructure-free way, can be used to collect data including real-time
locations and velocities of vehicles for road travel speed estimation, which is
a quite low-cost way. However, vehicular crowdsensing data is always
coarse-grained. This coarseness can lead to the incompleteness of travel
speeds. Aiming to handle this problem as well as estimate travel speed
accurately, in this paper, we propose an approach named STC that exploits the
spatial-temporal correlation among travel speeds for roads by introducing the
time-lagged cross correlation function. The time lagging factor describes the
time consumption of traffic feature diffusion along roads. To properly
calculate cross correlation, we novelly make the determination of the time
lagging factor self-adaptive by recording the locations of vehicles at
different roads. Then, utilizing the local stationarity of cross correlation,
we further reduce the problem of single-road travel speed vacancy completion to
a minimization problem. Finally, we fill all the vacancies of travel speed for
roads in a recursive way using the geometric structure of road net. Elaborate
experiments based on real taxi trace data show that STC can settle the
incompleteness problem of vehicle crowdsensing data based travel speed
estimation and ensure the accuracy of estimated travel speed better, in
comparison with representative existing methods such as KNN, Kriging and ARIMA.
