A Customized Data Fusion Tensor Approach for Interval-Wise Missing Network Volume Imputation

Traffic missing data imputation is a fundamental demand and crucial application for real-world intelligent transportation systems. The wide imputation methods in different missing patterns have demonstrated the superiority of tensor learning by effectively characterizing complex spatiotemporal correlations. However, interval-wise missing volume scenarios remain a challenging topic, in particular for long-term continuous missing and high-dimensional data with complex missing mechanisms and patterns. In this paper, the authors propose a customized tensor decomposition framework, named the data fusion CANDECOMP/PARAFAC (DFCP) tensor decomposition, to combine vehicle license plate recognition (LPR) data and cellphone location (CL) data for the interval-wise missing volume imputation on urban networks. Benefiting from the unique advantages of CL data in the wide spatiotemporal coverage and correlates highly with real-world traffic states, it is fused into vehicle license plate recognition (LPR) data imputation. They are regarded as data types dimension, combined with other dimensions (different segments, time, days); the authors innovatively design a 4-way low-n-rank tensor decomposition for data reconstruction. Furthermore, to deal with the diverse disturbances in different data dimensions, they derive a regularization penalty coefficient in data imputation. Different from existing regularization schemes, wthey further introduce Bayesian optimization (BO) to enhance the performance in the non-convexity of the objective function in their regularized hyperparametric solutions during tensor decomposition. Numerical experiments highlight that their proposed method, combining CL and LPR data, significantly outperforms the imputation method using LPR data only. And a sensitivity analysis with varying missing length and rate scenarios demonstrates the robustness of model performance.

• Record URL:
  • https://doi.org/10.1109/TITS.2023.3289193
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Xing, Jiping
  • Liu, Ronghui
  • Anish, Khadka
  • Liu, Zhiyuan
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 12107-12122
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Data fusion; Time intervals; Traffic data; Traffic volume
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01909567
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 23 2024 11:30AM