Low-rank hankel tensor completion for traffic speed estimation

McGill University, Feb. 2021 ~ Jun. 2021
Advisor: Prof. Lijun Sun
Co-worker: Xudong Wang , Yuankai Wu
Resources: ar5iv GitHub

• This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors. TSE can be considered a spatiotemporal interpolation problem in which the evolution of traffic variables (e.g., speed/density) is governed by traffic flow dynamics (e.g., partial differential equations).
• We consider TSE as a spatiotemporal matrix completion/interpolation problem, and apply spatiotemporal Hankel delay embedding to transforms the original incomplete matrix to a fourth-order tensor.
• The proposed framework only involves two hyperparameters—spatial and temporal window lengths, which are easy to set given the degree of data sparsity.