traffic simulation software is used by transportation researchers and
engineers to design and evaluate changes to roadways. These simulators are
driven by models of microscopic driver behavior from which macroscopic measures
like flow and congestion can be derived. Many models are desi
本文提出了一种新颖的基于物理信息和深度自编码器的学习校准方法,通过结合经典的深度自编码器和交通流动模型,以相对于传统基于优化方法的方法可比甚至更好的性能处理流量和速度测量来推出合理的交通参数。同时,引入去噪自编码器来应对有缺失值的交通数据的处理,我们通过 I-210 E 的案例研究来验证了我们的方法。