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OD matrix estimation is a critical problem in the transportation domain. Theprinciple method uses the traffic sensor measured information such as trafficcounts to estimate the traffic demand represented by the OD matrix. The problemis divided into two categories: static OD matrix estimation and dynamic ODmatrices sequence(OD sequence for short) estimation. The above two face theunderdetermination problem caused by abundant estimated parameters andinsufficient constraint information. In addition, OD sequence estimation alsofaces the lag challenge: due to different traffic conditions such ascongestion, identical vehicle will appear on different road sections during thesame observation period, resulting in identical OD demands correspond todifferent trips. To this end, this paper proposes an integrated method, whichuses deep learning methods to infer the structure of OD sequence and usesstructural constraints to guide traditional numerical optimization. Ourexperiments show that the neural network(NN) can effectively infer thestructure of the OD sequence and provide practical constraints for numericaloptimization to obtain better results. Moreover, the experiments show thatprovided structural information contains not only constraints on the spatialstructure of OD matrices but also provides constraints on the temporalstructure of OD sequence, which solve the effect of the lagging problem well.

A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence