A data-fusion spatiotemporal matrix factorization approach for citywide traffic flow estimation and prediction under insufficient detection

Abstract

Citywide traffic flow is essential for the urban traffic planning, traffic signal control, and automotive emission management. However, it is impractical to directly detect the traffic flow of each road segment due to the unaffordable costs of detector installment and maintenance. Under the insufficient detection, the traffic flows of road segments without detectors are totally unknown. Thus, it is imperative to estimate existing values and predict future values for traffic flows of unobserved road segments. To solve this conundrum, we propose a data-fusion spatiotemporal matrix factorization (DSTMF) approach. Firstly, the Gaussian priors are introduced to latent matrices of a matrix factorization model. Secondly, we develop an adaptive spatial regularization term, which models the dependencies of road segments through the knowledge from floating cars’ speed. Thirdly, a learnable autoregressive temporal regularization term is proposed to capture the temporal dependencies of traffic flow and predict future values. Finally, DSTMF is formulated as a quadratic programming and we design an optimization algorithm based on the alternating least squares to solve it. Validated on two real-world large-scale traffic datasets with almost 600 road segments, our method is consistently superior to well-known benchmark models for both the estimation and prediction tasks.