Counterfactual Explanations for Deep Learning-Based Traffic Forecasting

• URL: http://arxiv.org/abs/2405.00456v1
• Date: Wed, 1 May 2024 11:26:31 GMT
• Title: Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
• Authors: Rushan Wang, Yanan Xin, Yatao Zhang, Fernando Perez-Cruz, Martin Raubal,
• Abstract summary: This study aims to leverage an Explainable AI approach, counterfactual explanations, to enhance the explainability and usability of deep learning-based traffic forecasting models. The study first implements a deep learning model to predict traffic speed based on historical traffic data and contextual variables. Counterfactual explanations are then used to illuminate how alterations in these input variables affect predicted outcomes.
• Score: 42.31238891397725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results difficult to interpret by users. This study aims to leverage an Explainable AI approach, counterfactual explanations, to enhance the explainability and usability of deep learning-based traffic forecasting models. Specifically, the goal is to elucidate relationships between various input contextual features and their corresponding predictions. We present a comprehensive framework that generates counterfactual explanations for traffic forecasting and provides usable insights through the proposed scenario-driven counterfactual explanations. The study first implements a deep learning model to predict traffic speed based on historical traffic data and contextual variables. Counterfactual explanations are then used to illuminate how alterations in these input variables affect predicted outcomes, thereby enhancing the transparency of the deep learning model. We investigated the impact of contextual features on traffic speed prediction under varying spatial and temporal conditions. The scenario-driven counterfactual explanations integrate two types of user-defined constraints, directional and weighting constraints, to tailor the search for counterfactual explanations to specific use cases. These tailored explanations benefit machine learning practitioners who aim to understand the model's learning mechanisms and domain experts who seek insights for real-world applications. The results showcase the effectiveness of counterfactual explanations in revealing traffic patterns learned by deep learning models, showing its potential for interpreting black-box deep learning models used for spatiotemporal predictions in general.

Related papers

• Traj-Explainer: An Explainable and Robust Multi-modal Trajectory Prediction Approach [12.60529039445456]
  Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles. Existing research often neglects the consideration of the joint reasoning of scenario agents and lacks interpretability in trajectory prediction models. An explainability-oriented trajectory prediction model is designed in this work, named Explainable Diffusion Conditional based Multimodal Trajectory Prediction Traj-Explainer.
  arXiv  Detail & Related papers  (2024-10-22T08:17:33Z)
• XForecast: Evaluating Natural Language Explanations for Time Series Forecasting [72.57427992446698]
  Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions. Traditional explainable AI (XAI) methods, which underline feature or temporal importance, often require expert knowledge. evaluating forecast NLEs is difficult due to the complex causal relationships in time series data.
  arXiv  Detail & Related papers  (2024-10-18T05:16:39Z)
• Towards Explainable Traffic Flow Prediction with Large Language Models [36.86937188565623]
  We propose a Traffic flow Prediction model based on Large Language Models (LLMs) to generate explainable traffic predictions. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions.
  arXiv  Detail & Related papers  (2024-04-03T07:14:15Z)
• Interpretable Machine Learning for Weather and Climate Prediction: A Survey [24.028385794099435]
  We review current interpretable machine learning approaches applied to meteorological predictions. Design inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We discuss research challenges around achieving deeper mechanistic interpretations aligned with physical principles.
  arXiv  Detail & Related papers  (2024-03-24T14:23:35Z)
• Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis [6.606409729669314]
  Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. We conduct a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary.
  arXiv  Detail & Related papers  (2023-09-21T11:54:20Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)
• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors [3.4253416336476246]
  We focus on the analysis of counterfactual, contrastive explanations. We propose a new back translation-inspired evaluation methodology. We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
  arXiv  Detail & Related papers  (2023-05-26T16:04:28Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Deducing neighborhoods of classes from a fitted model [68.8204255655161]
  In this article a new kind of interpretable machine learning method is presented. It can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed.
  arXiv  Detail & Related papers  (2020-09-11T16:35:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.