Enhanced Route Planning with Calibrated Uncertainty Set
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929757671653376 |
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| author | Tang, Lingxuan Luo, Rui Zhou, Zhixin Colombo, Nicolo |
| author_facet | Tang, Lingxuan Luo, Rui Zhou, Zhixin Colombo, Nicolo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Route Planning with Calibrated Uncertainty Set Tang, Lingxuan Luo, Rui Zhou, Zhixin Colombo, Nicolo Machine Learning 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. |
| title | Enhanced Route Planning with Calibrated Uncertainty Set |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.10088 |