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The study focuses on estimating and predicting time-varying origin todestination (OD) trip tables for a dynamic traffic assignment (DTA) model. Abi-level optimisation problem is formulated and solved to estimate OD flowsfrom pre-existent demand matrix and historical traffic flow counts. Theestimated demand is then considered as an input for a time series OD demandprediction model to support the DTA model for short-term traffic conditionforecasting. Results show a high capability of the proposed OD demandestimation method to reduce the DTA model error through an iterative solutionalgorithm. Moreover, the applicability of the OD demand prediction approach isinvestigated for an incident analysis application for a major corridor inSydney, Australia.

Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications