发表机构
IFP Energies nouvelles; Université Gustave Eiffel; Khalifa University（法国石油与新能源研究院; 古斯塔夫·埃菲尔大学; 哈里发大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出链路级学习框架，利用区域数据估计交通流量，将其视为稀疏监督下的空间外泛化问题。通过容量感知公式嵌入交通理论约束，实验表明该框架在空间分布转移下优于基线，提升了交通流量估计的泛化能力。
AI 中文摘要
全网络交通流量估计通常依赖于固定传感器的测量传播，性能高度依赖传感器密度，限制了在稀疏测量网络中的部署。我们提出了一个链路级学习框架，仅根据广泛可用的区域数据（包括探测速度剖面、道路和拓扑描述符以及气象观测）来估计每小时交通流量。从稀疏传感器测量中学习监督局部映射，并在两种泛化设置下进行评估：网络内（训练网络内的未见链路）和网络间（未见城市）。这种公式将交通流量估计框架化为稀疏监督下的空间分布外泛化问题。为了增强空间鲁棒性，我们引入了一种容量感知公式，将流量建模为特定链路结构容量与每小时状态感知利用率的乘积，将交通理论约束直接嵌入学习过程。在两种泛化设置下的大量实验表明，所提出的结构约束在空间分布转移下始终优于现有基线。
英文摘要
Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deployment in sparsely instrumented networks. We propose a link-level learning framework that estimates hourly traffic volumes from widely available territorial data only, including probe speed profiles, road and topological descriptors, along with weather observations. A supervised local mapping is learned from sparse sensor measurements and evaluated under two generalization settings: intra-network (unseen links within the training network) and inter-network (unseen city). This formulation frames traffic volume estimation as a spatial out-of-distribution generalization problem under sparse supervision. To enhance spatial robustness, we introduce a capacity-aware formulation that models volume as the product of a link-specific structural capacity and an hourly regime-aware utilization ratio, embedding traffic-theoretic constraints directly into the learning process. Extensive experiments in both generalization settings demonstrate that the proposed structural constraints consistently outperform a state-of-the-art baseline under spatial distribution shift.
