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 are always infrastructure-based and therefore financial costly (for example, cameras and loop detectors) . 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 speed estimation . Aiming to handle the incompleteness problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC in which spatial-temporal correlation among travel speeds of roads is leveraged. More specifically, in STC, we use cross correlation function to quantize the correlation among roads. Noticing the directional patterns of traffic feature diffusion  , we novelly make the determination of the time lagging factor in cross correlation calculation self-adaptive by using vehicle tracking . 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 geomet-rical structure of road net. Elaborate experiments based on real taxi trace data show that STC can commendably settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed  , in comparison with representative existing methods  .
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 .
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are always infrastructure-based and
always depend on dedicated sensors and supporting infrastructures, and are
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(for example, cameras and loop detectors)
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speed estimation
speeds
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the incompleteness
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in which
that exploits the
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of roads is leveraged. More specifically, in STC, we use
for roads by introducing the time-lagged
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to quantize the correlation among roads. Noticing the directional patterns
. The time lagging factor describes the time consumption
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along roads. To properly calculate cross correlation
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in cross correlation calculation
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using vehicle tracking
recording the locations of vehicles at different roads
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geomet-rical
geometric
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commendably
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better
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such as KNN, Kriging and ARIMA