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

TL;DR

This paper provides a comprehensive survey and benchmarking framework for spatial-temporal traffic data imputation models, addressing key gaps in taxonomy, evaluation, and comparative analysis to guide practical applications.

Contribution

It introduces practice-oriented taxonomies, a unified benchmarking pipeline, and systematic comparisons of 11 models across multiple dimensions in traffic data imputation.

Findings

Catalogs real-world traffic data loss scenarios.

Evaluates 11 models across various missing patterns and rates.

Provides insights on model effectiveness, efficiency, and robustness.

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.