发表机构
Institute for Transport Planning and Systems, ETH Zurich; School of Transportation, Jilin University（苏黎世联邦理工学院交通规划与系统研究所; 吉林大学交通学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传感器稀疏导致多年路网AADT估计困难的问题，提出融合稀疏检测器与宏观模型的时空互补特征传播框架，在苏黎世验证误差低于10%。
AI 中文摘要
年平均日交通量（AADT）的估计对于交通规划和基础设施维护至关重要，然而由于物理传感器的高成本和空间稀疏性，获取整个城市路网多年间的准确数值仍然具有挑战性。本研究提出了一种新颖的时空互补特征传播框架，该框架利用了两种不同数据源的优势：空间稀疏但时间密集的环形线圈检测器数据，以及空间完整但时间稀疏的宏观交通模型。该方法重点在于有向图上的特征传播算法，该算法被表述为考虑残差的泊松能量最小化问题。标准二元邻接矩阵被替换为流量比矩阵，以捕捉交叉口处真实的车辆转向比例。在苏黎世市进行的验证表明，该算法具有较高的计算效率，能在数分钟内收敛。结果表明，该框架有效地调和了理论模型与经验真值，归一化平均绝对误差低于10%。这种可扩展的方法通过结合现实世界中有限的传感器覆盖和交通模型，为时空路网范围的AADT估计提供了一种可行的解决方案。
英文摘要
The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values for an entire urban network across multiple years remains challenging due to the high cost and spatial sparsity of physical sensors. This research proposes a novel spatio-temporally complementary feature propagation framework that leverages the strengths of two distinct data sources: spatially sparse but temporally dense loop detector data, and a spatially complete but temporally sparse macroscopic transportation model. The methodology highlights a feature propagation algorithm on directed graphs, formulated as a Poisson energy minimization considering residues. The standard binary adjacency matrix is replaced with flow ratio matrices to capture real-world vehicle turn ratios at intersections. Validated in the city of Zurich, the algorithm demonstrates high computational efficiency, achieving convergence within minutes. Results indicate that the framework effectively reconciles theoretical models with empirical ground truths, yielding a normalized mean absolute error below $10\%$. This scalable approach provides a feasible solution for spatio-temporal network-wide AADT estimation through combining real-world limited sensor coverage and traffic models.
