AI 中文总结
该研究针对极端天气下公共交通客流预测难题，提出基于欧姆定律的OhmicFlow框架，结合深圳地铁数据验证其在预测精度及多方面性能上优于基线方法。
AI 中文摘要
随着气候变化加剧，极端天气事件（EWEs）日益扰乱公共交通系统的供需平衡。准确可靠地预测起讫点（OD）客流对于及时应急响应至关重要，但在异常条件下仍存在困难。尽管现有的物理信息方法可提升模型鲁棒性，但对于极端天气中断下的公共交通系统仍不足，此类系统中客流波动由两类因素共同塑造：一是极端天气直接导致的潜在需求再分配（即直接效应），二是极端天气引发的供给收缩与拥堵效应导致的出行阻抗增加（即间接效应）。为解决这些局限，我们首先将公共交通网络概念化为电路，把潜在需求视为电压、出行阻抗视为电阻、客流视为电流，进而提出OhmicFlow框架，通过欧姆定律联合预测中断下的这些变量。具体而言，采用未来感知时空骨干网络作为电流表预测中断客流，其复本被用作旁路电压表，通过输入中控制的阻抗并行反事实推断潜在需求；进一步采用热敏电阻类比建模可预见极端天气中断下的出行阻抗，通过耦合供给收缩与拥堵效应实现动态估计；引入多目标损失函数拟合观测数据，同时强制欧姆约束。基于覆盖17次极端天气事件的10年深圳地铁数据开展的实证实验表明，OhmicFlow在各类时间顺序训练设置下均优于多种基线方法，预测误差更低，同时提升了不确定性校准、鲁棒性、可迁移性与可解释性。
英文摘要
As climate change intensifies, extreme weather events (EWEs) increasingly disrupt the supply-demand balance of mass transit systems. Accurate and reliable prediction of origin-destination (OD) passenger flow is essential for timely emergency response, but remains difficult under abnormal conditions. Although existing physics-informed methods can enhance model robustness, they remain insufficient for transit systems under extreme weather disruptions, where passenger flow fluctuations are jointly shaped by the redistribution of latent demand as a direct result of EWEs (i.e., direct effects) and the increase in travel impedance due to EWE-induced supply contraction and congestion effects (i.e., indirect effects). To address these limitations, we first conceptualize the transit network as an electrical circuit, treating latent demand as voltage, travel impedance as resistance and passenger flow as current, and then propose the OhmicFlow framework to jointly predict these variables under disruptions through Ohm's law. Specifically, a future-aware spatiotemporal backbone is used as an ammeter to predict disrupted passenger flow, and its replica is reused as a bypass voltmeter with impedance controlled in the inputs to infer latent demand counterfactually and in parallel. Travel impedance under foreseeable EWE disruptions is further modeled using a thermistor analogy, enabling dynamic estimation by coupling supply contraction with congestion effects. A multi-objective loss is incorporated to fit observed data while enforcing the Ohmic constraint. Empirical experiments based on 10 years of Shenzhen Metro data covering 17 EWEs show that OhmicFlow consistently outperforms various baseline methods, achieving lower prediction errors across various chronological training settings while improving uncertainty calibration, robustness, transferability, and interpretability.
