Counterfactual Explanations for Deep Learning-Based Traffic Forecasting

TL;DR

This paper introduces a framework using counterfactual explanations to improve the interpretability of deep learning models in traffic forecasting, helping users understand how input features influence predictions.

Contribution

It presents a novel scenario-driven counterfactual explanation method tailored for traffic forecasting models, enhancing transparency and usability for practitioners and domain experts.

Findings

Counterfactual explanations reveal key traffic patterns learned by models.

Scenario-driven explanations can be customized with user constraints.

The approach improves interpretability of deep learning-based traffic predictions.

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.