发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对交通分配问题中GNN模型空间泛化差的问题，提出网络无关初始化层GUIDED，通过在虚拟链路注入需求标量属性标准化输入空间。实验表明其能提升模型性能、鲁棒性及参数效率，减少训练时间，为相关空间问题提供通用蓝图。
AI 中文摘要
交通分配问题是交通规划中一个基本但计算成本高昂的组成部分。虽然图神经网络已成为快速、数据驱动的替代方法，但其实际部署受到空间泛化差距的严重限制。标准模型依赖于将出行需求与固定网络拓扑相关联的转导特征初始化，阻碍了向新城市环境的无缝迁移。本研究提出了一个网络无关的初始化层，即几何无约束归纳需求嵌入（GUIDED）。通过在辅助虚拟链路上将出行需求作为标量属性注入，而非特定节点特征，该模块化框架使输入空间标准化。实验表明，集成GUIDED层的异构图注意力网络在单网络任务上保持先进预测精度，对分布外需求模式有更强鲁棒性，在数据稀缺时也优于基线。该特征初始化实现高效参数的域适应，优化的散射操作使训练时间减少约50%，为广义起讫点空间问题提供了通用蓝图。
英文摘要
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap. Standard models rely on transductive feature initializations that tie travel demand to fixed network topologies, preventing seamless transfer to new urban environments. To overcome this structural limitation, this research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding (GUIDED). By injecting travel demand as a scalar attribute on auxiliary virtual links rather than as specific node features, this modular framework standardizes the input space regardless of network scale. Extensive experimental evaluation across multiple urban topologies demonstrates that a Heterogeneous Graph Attention Network (HetGAT) model integrated with the proposed GUIDED layer maintains state-of-the-art predictive accuracy on single-network tasks, while demonstrating superior robustness to out-of-distribution demand patterns and maintaining a distinct performance advantage over the baseline even under severe data scarcity. Notably, the proposed feature initialization enables highly parameter-efficient domain adaptation for inter-network transfer learning without artificial input homogenization, establishing a robust foundation for truly inductive models. At the same time, the optimized scatter operations of the initialization layer yield an approximate 50% reduction in training time per epoch compared to the baseline approach. Furthermore, while demonstrated on vehicular traffic, this fundamental abstraction of spatial topology provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.
