mlsanityData to 14 Oct 2026

Machine Unlearning of Traffic State Estimation and Prediction

arXiv 2507.17984 ↗First posted 23 Jul 2025cs.LG cs.AIcited by 0

On the map: LLM safety and security → Machine unlearning

Abstract (arXiv)

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TSEP-Machine Unlearning TSEP-which enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data.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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Machine unlearning, one of 12 directions in LLM safety and security. Builds on 17 of its 45 references that are in the map. First posted on arXiv 23 Jul 2025.

Builds on

17 of 45 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition13 Jun
  2. 2024Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models31 Mar
  3. 2024Corrective Machine Unlearning21 Feb
  4. 2023Fast Model Debias with Machine Unlearning19 Oct
  5. 2023Fair Machine Unlearning: Data Removal while Mitigating Disparities27 Jul
  6. 2022A Survey of Machine Unlearning6 Sep
  7. 2021Machine Unlearning of Features and Labels26 Aug

Other references · 10

  1. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  2. 2019Machine Unlearningcited by 1,061
  3. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  4. 2020Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observationscited by 180
  5. 2020DeltaGrad: Rapid retraining of machine learning modelscited by 149
  6. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146
  7. 2021Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimationcited by 12
  8. 2020Securing Vehicle-to-Everything (V2X) Communication Platformscited by 9
  9. 2023Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Modelscited by 3
  10. 2024FairTP: A Prolonged Fairness Framework for Traffic Predictioncited by 3

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Learn to Unlearn: A Survey on Machine Unlearning
  2. 2026Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year
  3. 2024A Review on Machine Unlearning
  4. 2026TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification
  5. 2026Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
  6. 2024From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
  7. 2026Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
  8. 2024A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks