我们提出一种物理信息学习框架，用于从稀疏轨迹数据中重建交通密度。该方法将二阶Aw-Rascle-Zhang模型与一阶训练阶段相结合，以估计平衡速度。我们在SUMO仿真中分别于平衡态与瞬态交通场景下对该方法进行评估。结果表明：在稳态条件下，学习平衡速度可提升密度重建精度；但在瞬态场景下，由于平衡假设失效，该策略变得不稳定。相比之下，二阶模型在非平衡条件下始终比一阶方法提供更准确、更鲁棒的重建结果。

We propose a Physics Informed Learning framework for reconstructing traffic density from sparse trajectory data. The approach combines a second-order Aw-Rascle and Zhang model with a first-order training stage to estimate the equilibrium velocity. The method is evaluated in both equilibrium and transient traffic regimes using SUMO simulations. Results show that while learning the equilibrium velocity improves reconstruction under steady state conditions, it becomes unstable in transient regimes due to the breakdown of the equilibrium assumption. In contrast, the second-order model consistently provides more accurate and robust reconstructions than first-order approaches, particularly in nonequilibrium conditions.