An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data

• URL: http://arxiv.org/abs/2412.04733v1
• Date: Fri, 06 Dec 2024 02:47:54 GMT
• Title: An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
• Authors: Shengnan Guo, Tonglong Wei, Yiheng Huang, Miaomiao Zhao, Ran Chen, Yan Lin, Youfang Lin, Huaiyu Wan,
• Abstract summary: This work aims to provide a holistic understanding of traffic data imputation research and serve as a practical guideline.<n>We first propose practice-oriented for missing patterns and imputation models, systematically identifying all possible forms of real-world traffic data loss.<n> Furthermore, we introduce a unified benchmarking pipeline to comprehensively evaluate 10 representative models across various missing patterns and rates.
• Score: 20.294382826251994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services. Despite significant advancements in this field, selecting the most suitable model for practical applications remains challenging due to three key issues: 1) incomprehensive consideration of missing patterns that describe how data loss along spatial and temporal dimensions, 2) the lack of test on standardized datasets, and 3) insufficient evaluations. To this end, we first propose practice-oriented taxonomies for missing patterns and imputation models, systematically identifying all possible forms of real-world traffic data loss and analyzing the characteristics of existing models. Furthermore, we introduce a unified benchmarking pipeline to comprehensively evaluate 10 representative models across various missing patterns and rates. This work aims to provide a holistic understanding of traffic data imputation research and serve as a practical guideline.

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