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.
@article{arxiv.2503.10088,
title = {Enhanced Route Planning with Calibrated Uncertainty Set},
author = {Lingxuan Tang and Rui Luo and Zhixin Zhou and Nicolo Colombo},
journal= {arXiv preprint arXiv:2503.10088},
year = {2025}
}
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arXiv admin note: text overlap with arXiv:2406.08281