505 research outputs found
Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
Traffic speed is central to characterizing the fluidity of the road network.
Many transportation applications rely on it, such as real-time navigation,
dynamic route planning, and congestion management. Rapid advances in sensing
and communication techniques make traffic speed detection easier than ever.
However, due to sparse deployment of static sensors or low penetration of
mobile sensors, speeds detected are incomplete and far from network-wide use.
In addition, sensors are prone to error or missing data due to various kinds of
reasons, speeds from these sensors can become highly noisy. These drawbacks
call for effective techniques to recover credible estimates from the incomplete
data. In this work, we first identify the issue as a spatiotemporal kriging
problem and propose a Laplacian enhanced low-rank tensor completion (LETC)
framework featuring both lowrankness and multi-dimensional correlations for
large-scale traffic speed kriging under limited observations. To be specific,
three types of speed correlation including temporal continuity, temporal
periodicity, and spatial proximity are carefully chosen and simultaneously
modeled by three different forms of graph Laplacian, named temporal graph
Fourier transform, generalized temporal consistency regularization, and
diffusion graph regularization. We then design an efficient solution algorithm
via several effective numeric techniques to scale up the proposed model to
network-wide kriging. By performing experiments on two public million-level
traffic speed datasets, we finally draw the conclusion and find our proposed
LETC achieves the state-of-the-art kriging performance even under low
observation rates, while at the same time saving more than half computing time
compared with baseline methods. Some insights into spatiotemporal traffic data
modeling and kriging at the network level are provided as well
Matrix completion and extrapolation via kernel regression
Matrix completion and extrapolation (MCEX) are dealt with here over
reproducing kernel Hilbert spaces (RKHSs) in order to account for prior
information present in the available data. Aiming at a faster and
low-complexity solver, the task is formulated as a kernel ridge regression. The
resultant MCEX algorithm can also afford online implementation, while the class
of kernel functions also encompasses several existing approaches to MC with
prior information. Numerical tests on synthetic and real datasets show that the
novel approach performs faster than widespread methods such as alternating
least squares (ALS) or stochastic gradient descent (SGD), and that the recovery
error is reduced, especially when dealing with noisy data
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