联合信息论与图学习框架用于传感器布置与宏观交通状态重构

中文摘要 AI 辅助

大型城市网络中的交通传感器布置必须在交通监测需求与有限的安装、维护和数据管理预算之间取得平衡。本研究开发了一个框架，用于在未观测路段重构流量和密度以及宏观基本图（MFD）的同时，保留网络范围的交通信息。该框架将归一化互信息（NMI）与归纳图神经网络克里金法（IGNNK）相结合。NMI量化了联合流量-密度状态之间的滞后感知依赖性，而一种贪心算法通过平衡加权信息覆盖与冗余来选择传感器。基于IGNNK的双向图扩散模型重构未观测路段状态并生成MFD。该框架使用芝加哥网络的七小时动态交通分配模拟数据，与主成分分析（PCA）基准进行了评估，该网络包含4,805条有向路段和420个一分钟间隔，并划分为训练、验证和独立测试子集。PCA和NMI-IGNNK的重构性能在0.5%至30%的传感器比例范围内变化显著。PCA在0.5%-3%范围内通常产生较低的均方根误差（RMSE），但其误差非单调且在2%处达到最小值。在3%时，PCA和NMI-IGNNK的归一化联合RMSE相似（分别为0.100和0.105）。NMI-IGNNK在高达30%的覆盖率下仍然适用，并且通常随着覆盖率增加而改善，将测试子集的归一化联合RMSE从0.5%时的0.212降至30%时的0.043，在整个分析期间趋势一致。该框架将基于联合流量-密度状态的滞后感知信息论传感器布置与基于有向图的重构相结合，使得在大型网络中无需对传感器数量设置严格限制即可支持广泛的传感器预算。

英文摘要

Traffic sensor placement in large urban networks must balance traffic monitoring needs with limited installation, maintenance, and data-management budgets. This study develops a framework for preserving network-wide traffic information while reconstructing flow and density at unobserved links and the macroscopic fundamental diagram (MFD). The framework combines normalized mutual information (NMI) with Inductive Graph Neural Network Kriging (IGNNK). NMI quantifies lag-aware dependence between joint flow-density states, while a greedy algorithm selects sensors by balancing weighted information coverage against redundancy. An IGNNK-based bidirectional graph-diffusion model reconstructs unobserved link states and generates the MFD. The framework was evaluated against a principal component analysis (PCA) benchmark using seven hours of dynamic traffic assignment simulation data for the Chicago network, with 4,805 directed links and 420 one-minute intervals split into training, validation, and independent testing subsets. Reconstruction performance for both PCA and NMI-IGNNK varied substantially over the sensor ratios of 0.5% to 30%. PCA generally yielded lower root mean squared error (RMSE) over the 0.5%-3% range, but its errors were nonmonotonic and reached their minimum at 2%. At 3%, normalized joint RMSE was similar for PCA and NMI-IGNNK (0.100 and 0.105, respectively). NMI-IGNNK remained applicable up to 30% coverage and generally improved as coverage increased, reducing testing-subset normalized joint RMSE from 0.212 at 0.5% to 0.043 at 30%, with consistent trends over the complete analysis period. The framework integrates lag-aware, information-theoretic sensor placement based on joint flow-density states with directed graph-based reconstruction, enabling a broad range of sensor budgets in large-scale networks without strict limits on the number of sensors.

发表机构

• University of Connecticut（康涅狄格大学）

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