An Incomplete Tensor Tucker decomposition based Traffic Speed Prediction Method
This addresses missing data recovery for traffic speed prediction in intelligent transportation systems, but appears incremental as it combines existing techniques (Tucker decomposition and PID control).
The paper tackles the problem of missing traffic speed data in intelligent transport systems by proposing a Tucker decomposition-based latent factorization-of-tensors model with a PID controller-enhanced SGD solver, achieving significant efficiency gains and competitive prediction accuracy on two city traffic datasets.
In intelligent transport systems, it is common and inevitable with missing data. While complete and valid traffic speed data is of great importance to intelligent transportation systems. A latent factorization-of-tensors (LFT) model is one of the most attractive approaches to solve missing traffic data recovery due to its well-scalability. A LFT model achieves optimization usually via a stochastic gradient descent (SGD) solver, however, the SGD-based LFT suffers from slow convergence. To deal with this issue, this work integrates the unique advantages of the proportional-integral-derivative (PID) controller into a Tucker decomposition based LFT model. It adopts two-fold ideas: a) adopting tucker decomposition to build a LFT model for achieving a better recovery accuracy. b) taking the adjusted instance error based on the PID control theory into the SGD solver to effectively improve convergence rate. Our experimental studies on two major city traffic road speed datasets show that the proposed model achieves significant efficiency gain and highly competitive prediction accuracy.