A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models

• URL: http://arxiv.org/abs/2412.04733v2
• Date: Fri, 17 Oct 2025 07:37:32 GMT
• Title: A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models
• Authors: Shengnan Guo, Tonglong Wei, Yiheng Huang, Yan Lin, Zekai Shen, Yujuan Dong, Junliang Lin, Youfang Lin, Huaiyu Wan,
• Abstract summary: Traffic data imputation is a critical preprocessing step in intelligent transportation systems.<n>Despite substantial progress in imputation models, model selection and development for practical applications remains challenging.<n>This paper proposes practice-oriented for traffic data missing patterns and imputation models, systematically cataloging real-world traffic data loss scenarios.
• Score: 26.91571883554539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic data imputation is a critical preprocessing step in intelligent transportation systems, underpinning the reliability of downstream transportation services. Despite substantial progress in imputation models, model selection and development for practical applications remains challenging due to three key gaps: 1) the absence of a model taxonomy for traffic data imputation to trace the technological development and highlight their distinct features. 2) the lack of unified benchmarking pipeline for fair and reproducible model evaluation across standardized traffic datasets. 3) insufficient in-depth analysis that jointly compare models across multiple dimensions, including effectiveness, computational efficiency and robustness. To this end, this paper proposes practice-oriented taxonomies for traffic data missing patterns and imputation models, systematically cataloging real-world traffic data loss scenarios and analyzing the characteristics of existing models. We further introduce a unified benchmarking pipeline to comprehensively evaluate 11 representative models across various missing patterns and rates, assessing overall performance, performance under challenging scenarios, computational efficiency, and providing visualizations. This work aims to provide a holistic perspective on traffic data imputation and to serve as a practical guideline for model selection and application in intelligent transportation systems.

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