用简单方法让交通预测模型具备概率能力，还能保持原有精度。
Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting
- 仅替换输出层为高斯混合模型，无需改训练流程。
- 在多个数据集上提升预测不确定性建模精度，优于单一模式基线。
- 适合关注交通风险评估与鲁棒性分析的研究者或城市管理者。
交通预测是时空建模中的挑战性任务，也是城市交通管理的关键环节。现有研究多聚焦于确定性预测，对交通动态中的不确定性和随机性关注不足。本文提出一种简洁通用的方法：仅将现有模型的最终输出层替换为新型高斯混合模型（GMM）层，即可将其转化为概率预测器。该修改不改变训练流程，仅需使用负对数似然（NLL）损失进行训练，无需额外辅助项或正则化。在多个交通数据集上的实验表明，该方法可从经典到现代模型架构均实现良好泛化，同时保持确定性性能。我们还提出基于累积分布和置信区间的系统性评估流程，验证了本方法在准确性与信息量上显著优于单模态或确定性基线。最后，通过对真实密集城市路网的深入分析，探讨了数据质量对不确定性量化的影响，并展示了方法在不完美数据条件下的鲁棒性。代码已开源。
原文摘要 · Abstract (English)
Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management. Current studies mainly focus on deterministic predictions, with limited considerations on the uncertainty and stochasticity in traffic dynamics. Therefore, this paper proposes an elegant yet universal approach that transforms existing models into probabilistic predictors by replacing only the final output layer with a novel Gaussian Mixture Model (GMM) layer. The modified model requires no changes to the training pipeline and can be trained using only the Negative Log-Likelihood (NLL) loss, without any auxiliary or regularization terms. Experiments on multiple traffic datasets show that our approach generalizes from classic to modern model architectures while preserving deterministic performance. Furthermore, we propose a systematic evaluation procedure based on cumulative distributions and confidence intervals, and demonstrate that our approach is considerably more accurate and informative than unimodal or deterministic baselines. Finally, a more detailed study on a real-world dense urban traffic network is presented to examine the impact of data quality on uncertainty quantification and to show the robustness of our approach under imperfect data conditions. Code available at https://github.com/Weijiang-Xiong/OpenSkyTraffic
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