用不确定性校准提升路径规划可靠性
Enhanced Route Planning with Calibrated Uncertainty Set
- 基于图自编码器的分位数回归结合置信预测
- 在真实交通场景中显著优于基线方法
- 适合智能交通系统与鲁棒决策研究者
本文研究概率预测方法在道路网络路径规划中的应用。提出一种结合置信预测技术的图自编码器分位数回归模型（CQR-GAE），通过提供覆盖保证，提升预测的可靠性和鲁棒性。利用CQR-GAE生成的不确定性集，在鲁棒优化框架下显著改进路径规划决策。在真实交通场景中的实验表明，该模型显著优于基线方法，为智能交通系统的发展提供了新路径。
原文摘要 · Abstract (English)
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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