Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization

Xinyu Chen (), Xi-Le Zhao () and Chun Cheng ()
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Xinyu Chen: Civil, Geological and Mining Engineering Department, Polytechnique Montréal, Montréal, Quebec H3T 1J4, Canada
Xi-Le Zhao: School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610056, China
Chun Cheng: School of Economics and Management, Dalian University of Technology, Dalian 116024, China

INFORMS Journal on Computing, 2025, vol. 37, issue 4, 945-961

Abstract: Forecasting urban traffic states is crucial to transportation network monitoring and management, playing an important role in the decision-making process. Despite the substantial progress that has been made in developing accurate, efficient, and reliable algorithms for traffic forecasting, most existing approaches fail to handle sparsity, high-dimensionality, and nonstationarity in traffic time series and seldom consider the temporal dependence between traffic states. To address these issues, this work presents a Hankel temporal matrix factorization (HTMF) model using the Hankel matrix in the lower dimensional spaces under a matrix factorization framework. In particular, we consider an alternating minimization scheme to optimize the factor matrices in matrix factorization and the Hankel matrix in the lower dimensional spaces simultaneously. To perform traffic state forecasting, we introduce two efficient estimation processes on real-time incremental data, including an online imputation (i.e., reconstruct missing values) and an online forecasting (i.e., estimate future data points). Through extensive experiments on the real-world Uber movement speed data set in Seattle, Washington, we empirically demonstrate the superior forecasting performance of HTMF over several baseline models and highlight the advantages of HTMF for addressing sparsity, nonstationarity, and short time series.

Keywords: urban transportation network; traffic state forecasting; machine learning; matrix factorization; Hankel matrix (search for similar items in EconPapers)
Date: 2025
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