  OD matrix estimation is a critical problem in the transportation domain. The
principle method uses the traffic sensor measured information such as traffic
counts to estimate the traffic demand represented by the OD matrix. The problem
is divided into two categories: static OD matrix estimation and dynamic OD
matrices sequence(OD sequence for short) estimation. The above two face the
underdetermination problem caused by abundant estimated parameters and
insufficient constraint information. In addition, OD sequence estimation also
faces the lag challenge: due to different traffic conditions such as
congestion, identical vehicle will appear on different road sections during the
same observation period, resulting in identical OD demands correspond to
different trips. To this end, this paper proposes an integrated method, which
uses deep learning methods to infer the structure of OD sequence and uses
structural constraints to guide traditional numerical optimization. Our
experiments show that the neural network(NN) can effectively infer the
structure of the OD sequence and provide practical constraints for numerical
optimization to obtain better results. Moreover, the experiments show that
provided structural information contains not only constraints on the spatial
structure of OD matrices but also provides constraints on the temporal
structure of OD sequence, which solve the effect of the lagging problem well.
