Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications
This work addresses traffic management and forecasting for urban planners and transportation engineers, but it is incremental as it builds on existing dynamic traffic assignment methods.
The study tackled the problem of estimating and predicting time-varying origin-destination trip tables for dynamic traffic assignment models, using a bi-level optimization approach to reduce model error through an iterative algorithm, and applied the prediction method to incident analysis in Sydney, Australia.
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then considered as an input for a time series OD demand prediction model to support the DTA model for short-term traffic condition forecasting. Results show a high capability of the proposed OD demand estimation method to reduce the DTA model error through an iterative solution algorithm. Moreover, the applicability of the OD demand prediction approach is investigated for an incident analysis application for a major corridor in Sydney, Australia.