  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.
