用神经微分方程直接建模交通出行矩阵，更准更快。
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
- 基于神经微分方程建模空间交互，直接在离散组合空间操作。
- 在剑桥和华盛顿特区的测试中，误差更低、覆盖真实数据更全。
- 计算成本仅为旧方法的一小部分，适合大规模模拟使用。
基于代理的模型（ABMs）在交通、经济和流行病学等领域广泛用于决策支持。其中核心是离散的起讫点矩阵，用于捕捉地点间的空间互动与出行次数。现有方法依赖对矩阵的连续近似，再进行人为离散化，导致无法有效利用部分观测统计量，难以探索离散组合空间上的多模态分布，并引入离散化误差。为此，我们提出一种计算高效框架，其复杂度随起讫点对数线性增长，直接在离散组合空间上操作，并通过嵌入空间交互的神经微分方程学习出行强度。该方法在重建误差和真实矩阵覆盖率上优于已有方法，计算成本大幅降低。我们在英国剑桥和美国华盛顿特区的大规模空间移动ABM中验证了其优势。
原文摘要 · Abstract (English)
Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. In these models, a central object of interest is the discrete origin-destination matrix which captures spatial interactions and agent trip counts between locations. Existing approaches resort to continuous approximations of this matrix and subsequent ad-hoc discretisations in order to perform ABM simulation and calibration. This impedes conditioning on partially observed summary statistics, fails to explore the multimodal matrix distribution over a discrete combinatorial support, and incurs discretisation errors. To address these challenges, we introduce a computationally efficient framework that scales linearly with the number of origin-destination pairs, operates directly on the discrete combinatorial space, and learns the agents' trip intensity through a neural differential equation that embeds spatial interactions. Our approach outperforms the prior art in terms of reconstruction error and ground truth matrix coverage, at a fraction of the computational cost. We demonstrate these benefits in large-scale spatial mobility ABMs in Cambridge, UK and Washington, DC, USA.
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