Well-calibrated traffic flow models are fundamental to understanding traffic
phenomena and designing control strategies. Traditional calibration has been
developed base on optimization methods. In this paper, we propose a novel
physics-informed, learning-based calibration approach that achieves
performances comparable to and even better than those of optimization-based
methods. To this end, we combine the classical deep autoencoder, an
unsupervised machine learning model consisting of one encoder and one decoder,
with traffic flow models. Our approach informs the decoder of the physical
traffic flow models and thus induces the encoder to yield reasonable traffic
parameters given flow and speed measurements. We also introduce the denoising
autoencoder into our method so that it can handles not only with normal data
but also with corrupted data with missing values. We verified our approach with
a case study of I-210 E in California.

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。