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arXiv · 2503.10088

Enhanced Route Planning with Calibrated Uncertainty Set

Lingxuan Tang·Rui Luo·Zhixin Zhou·Nicolo Colombo
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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.

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Lingxuan Tang, Rui Luo, Zhixin Zhou, Nicolo Colombo. 2025-03-13. Enhanced Route Planning with Calibrated Uncertainty Set. https://arxiv.org/abs/2503.10088

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