A Low-Rank Bayesian Temporal Matrix Factorization for the Transfer Time Prediction Between Metro and Bus Systems
Journal article, 2024

Accurate transfer time prediction and future transfer time information are important for both public transport operators and passengers. However, existing studies cannot effectively manage high-dimensional transfer time data, capture the complex nonlinearity of transfer time, or provide accurate transfer time information. This study provides a reliable prediction model called low-rank Bayesian temporal matrix factorization (LBTMF) to address these challenges. First, on the basis of a high-dimensional spatiotemporal matrix of transfer time data, we develop a low-rank temporal-regularized matrix factorization-based imputation module to capture spatial and temporal characteristics to replace missing transfer time data. Second, to further predict the transfer time with the imputation of missing data, we propose the spatiotemporal-based Bayesian temporal matrix factorization prediction module to recover hourly and daily regular characteristics to predict the transfer time at different metro stations during various periods. Finally, the comprehensive experimental findings suggest that the LBTMF model outperforms other excellent approaches in terms of imputation efficiency, prediction accuracy, and robustness.

high-dimensional transfer time

spatiotemporal characteristics

time-series prediction methods

Public transportation

low-rank matrix factorization

Author

Pan Wu

Chongqing Jiaotong University

Mingyang Pei

South China University of Technology

Tao Wang

Hefei University of Technology

Tsinghua University

Yang Liu

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Zhiyuan Liu

Southeast University

Lingshu Zhong

Sun Yat-Sen University

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 25 7 7206-7222

Subject Categories

Transport Systems and Logistics

DOI

10.1109/TITS.2023.3349211

More information

Latest update

7/20/2024