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

STC: Coarse-Grained Vehicular Data Based Travel Speed Sensing by Leveraging Spatial-Temporal Correlation