提出新模型OpFlow，让城市出行预测更抗环境变化。
OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction
- 分离出行需求与目的地选择机制，学习可迁移的偏好映射
- 在真实数据上提升37%的分布外预测准确率
- 适合交通规划、城市计算等需要跨区域泛化的场景
起点-终点（OD）流量预测是城市分析的核心，但基于原始流量计数训练的深度模型在分布外场景下仍易失效。根本原因在于原始计数无法区分可迁移的选择机制与环境特异的捷径。原始OD计数混合了起点产生需求量和需求分配至各终点两个维度。我们主张可迁移的本质是暴露-选择定律，即空间条件如何映射到相对目的地偏好。为此提出OpFlow框架：通过约束机制学习行中心的偏好势能，并结合独立校准的起点规模重建流量。在分布转移时，允许空间暴露与诱导分配变化，而条件映射（暴露状态到相对选择势能）保持不变。理论上刻画了可识别的行中心势能，表明经典空间交互律是受限对数势能特例。合成扰动与真实世界实验均验证，OpFlow在环境变化下显著提升鲁棒性。
原文摘要 · Abstract (English)
Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift. The core problem is that raw count supervision cannot distinguish transferable choice mechanisms from environment-specific shortcuts. Raw OD count mixes two objects: how much demand an origin produces and how that demand is allocated across destinations. We argue that the transferable object is the exposure-to-choice law that maps spatial conditions to relative destination preferences. We propose OpFlow, a mechanism-constrained framework that learns row-centered choice potentials and reconstructs flows by combining the induced allocation with a separately calibrated origin scale. Under distribution shift, spatial exposures and the induced allocations are allowed to vary; what transfers is the conditional map from exposure states to relative choice potentials. Theoretically, we characterize the identifiable row-centered potential and show that classical spatial interaction laws are restricted log-potential cases. Controlled synthetic shifts and a real-world experiment show OpFlow improves robustness under environment shifts.
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