通过最优传输整合结构和属性进行交通网络划分
Integrating Structure and Attributes for Transportation Network Partitioning via Optimal Transport
AI总结：
研究针对交通网络划分问题，提出基于距离的图表示和基于半松弛融合Gromov-Wasserstein差异的最优传输公式的灵活框架，能联合考虑网络结构与属性并控制权衡，在两个交通系统上评估，展示了依不同偏好调整划分结果的能力。
AI中文摘要：
交通网络划分对交通分析、模拟和出行模式识别等应用至关重要。然而，交通网络将结构信息与从标量指标到时间剖面的异构运营属性相结合。现有方法通常依赖预定义公式整合这些信息源，限制了控制其相对影响的能力。本文提出了一个灵活框架，用于划分表示为属性图的异构交通网络。该方法依赖基于距离的图表示和基于半松弛融合Gromov-Wasserstein差异的最优传输公式，能够联合考虑网络结构和属性，并明确控制它们的权衡。在两个具有不同特征的交通系统上对该方法进行了评估：一个用于面向交通划分的城市道路网络和一个用于识别基于使用情况的社区的自行车共享系统。结果表明该框架能够根据不同的结构和属性偏好调整划分结果。
英文摘要：
Transportation network partitioning is essential for applications such as traffic analysis, simulation, and mobility pattern identification. However, transportation networks combine structural information with heterogeneous operational attributes, ranging from scalar indicators to temporal profiles. Existing approaches generally rely on predefined formulations to integrate these sources of information, limiting the ability to control their relative influence. This paper proposes a flexible framework for partitioning heterogeneous transportation networks represented as attributed graphs. The proposed methodology relies on a distance-based graph representation and an optimal transport formulation based on the semi-relaxed Fused Gromov-Wasserstein discrepancy, enabling joint consideration of network structure and attributes with explicit control over their trade-off. The proposed methodology is evaluated on two transportation systems with distinct characteristics: an urban road network for traffic-oriented partitioning and a bicycle-sharing system for identifying usage-based communities. Results demonstrate the ability of the framework to adapt the resulting partitions according to different structural and attribute preferences.
