Machine Unlearning of Traffic State Estimation and Prediction

2025-07-23

AI Summary

Traffic State Estimation and Prediction (TSEP) faces critical challenges including privacy leakage from sensitive data, model contamination, degradation in temporal validity, and regulatory compliance with the “right to be forgotten,” which mandates efficient model-level removal of specific training samples. Method: This work pioneers the integration of machine unlearning into TSEP, proposing a selective unlearning framework that combines gradient correction with efficient model update mechanisms—enabling memory erasure of targeted samples (e.g., privacy-sensitive, anomalous, or outdated traffic data) without full retraining. Contribution/Results: The approach significantly reduces residual information leakage risks, enhances model security and dynamic adaptability, ensures compliance with GDPR and related data governance regulations, and improves public trust and operational reliability of intelligent transportation systems. Experimental validation demonstrates competitive prediction accuracy retention alongside rapid, scalable unlearning—marking the first systematic application of machine unlearning to spatiotemporal traffic modeling.

Technology Category

• Machine Learning: Privacy
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Data-driven traffic state estimation and prediction (TSEP) relies heavily on data sources that contain sensitive information. While the abundance of data has fueled significant breakthroughs, particularly in machine learning-based methods, it also raises concerns regarding privacy, cybersecurity, and data freshness. These issues can erode public trust in intelligent transportation systems. Recently, regulations have introduced the "right to be forgotten", allowing users to request the removal of their private data from models. As machine learning models can remember old data, simply removing it from back-end databases is insufficient in such systems. To address these challenges, this study introduces a novel learning paradigm for TSEP-Machine Unlearning TSEP-which enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data. By empowering models to "unlearn," we aim to enhance the trustworthiness and reliability of data-driven traffic TSEP.

Problem

• Machine unlearning for privacy-sensitive traffic data
• Addressing data freshness and cybersecurity in TSEP
• Enhancing trust in intelligent transportation systems

Innovation

• Machine Unlearning for TSEP privacy
• Selective forgetting of sensitive data
• Enhancing trust in traffic prediction