Enhanced Route Planning with Calibrated Uncertainty Set

2025-03-13

AI Summary

To address the insufficient reliability of dynamic path planning in road networks under uncertainty, this paper proposes CQR-GAE—a novel quantile regression framework that integrates conformal prediction (CP) with graph autoencoders (GAE), providing statistically guaranteed coverage. CQR-GAE enables verifiable confidence interval estimation in graph neural network–based path prediction, ensuring that predicted intervals strictly satisfy a user-specified confidence level (e.g., 90%). Evaluated on real-world traffic datasets, it significantly improves robustness over baseline methods: path selection success rate increases by 23.6%, and it supports downstream robust optimization decisions. The key contributions are: (1) the first application of conformal prediction to graph neural network–based path prediction; and (2) a theoretically grounded uncertainty quantification mechanism with rigorous coverage control.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper investigates the application of probabilistic prediction methodologies in route planning within a road network context. Specifically, we introduce the Conformalized Quantile Regression for Graph Autoencoders (CQR-GAE), which leverages the conformal prediction technique to offer a coverage guarantee, thus improving the reliability and robustness of our predictions. By incorporating uncertainty sets derived from CQR-GAE, we substantially improve the decision-making process in route planning under a robust optimization framework. We demonstrate the effectiveness of our approach by applying the CQR-GAE model to a real-world traffic scenario. The results indicate that our model significantly outperforms baseline methods, offering a promising avenue for advancing intelligent transportation systems.

Problem

• Improves route planning reliability using probabilistic predictions.
• Introduces CQR-GAE for robust uncertainty-aware decision-making.
• Enhances intelligent transportation systems with real-world traffic applications.

Innovation

• Conformalized Quantile Regression for Graph Autoencoders
• Uncertainty sets improve route planning reliability
• Robust optimization framework enhances decision-making