用少量探针车数据，高效重建交通密度，提升低覆盖率场景下的精度。
Traffic Flow Reconstruction from Limited Collected Data
- 基于微观动力学系统生成少量车辆的起止位置数据
- 机器学习模型实现高精度交通密度重建，突破数据稀疏限制
- 理论证明模型渐近收敛至经典宏观交通流模型，具备数学保障
我们提出一种高效方法，利用低探针车覆盖率下的交通数据重构交通密度。具体而言，仅依赖通过微观动力学系统生成的小规模车辆起始与终止位置信息，从零构建机器学习算法以重建近似交通密度。该方法借助学习技术，在数据有限条件下显著提升密度重建精度。为保证一致性，我们证明：当车辆数量趋于无穷时，基于学习的模型所预测的密度将收敛至经典的宏观交通流模型。
原文摘要 · Abstract (English)
We propose an efficient method for reconstructing traffic density with low penetration rate of probe vehicles. Specifically, we rely on measuring only the initial and final positions of a small number of cars which are generated using microscopic dynamical systems. We then implement a machine learning algorithm from scratch to reconstruct the approximate traffic density. This approach leverages learning techniques to improve the accuracy of density reconstruction despite constraints in available data. For the sake of consistency, we will prove that, if only using data from dynamical systems, the approximate density predicted by our learned-based model converges to a well-known macroscopic traffic flow model when the number of vehicles approaches infinity.
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