Enhanced Route Planning with Calibrated Uncertainty Set

• URL: http://arxiv.org/abs/2503.10088v1
• Date: Thu, 13 Mar 2025 06:31:42 GMT
• Title: Enhanced Route Planning with Calibrated Uncertainty Set
• Authors: Lingxuan Tang, Rui Luo, Zhixin Zhou, Nicolo Colombo,
• Abstract summary: We introduce the Conformalized Quantile Regression for Graph Autoencoders (CQR-GAE), which leverages the conformal prediction technique to offer a coverage guarantee.<n>We demonstrate the effectiveness of our approach by applying the CQR-GAE model to a real-world traffic scenario.
• Score: 7.7856491832713415
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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