Congratulations to Hao Wu for “satisfying the requirements for the award of the degree of Doctor of Philosophy at the University of Sydney.”
Lead Supervisor: Professor David Levinson.
Ensemble forecasting is a modeling approach that internalizes uncertainties, combining models with different assumptions or pattern recognition methods, data from different sources, and different methods of combining models. Compared to the prevalent single-model procedure, ensemble model predictions are more useful as decision support tools.
The use of ensemble forecasting has significantly improved forecast accuracy in weather forecasting, and is increasingly adopted in other fields. We find a lack of awareness, or application of ensemble models in transport, so the benefits of ensemble forecasting are not being realized.
In this research we establish a systematic framework for ensemble forecasting, and propose the `ensemble of ensembles’ to combine uncertainties in different ensemble methods. Ensemble models are applied to transport-related cases to examine the performance of different ensemble methods, and to compare ensemble models with single-model forecasts.
We find ensemble models can improve forecast accuracy by a notable degree beyond the best single model. Simple and weighted average ensemble models have mixed results. Meta-learner ensemble models provide significant improvement upon base models, but require sufficient training data to calibrate. We find the linear meta-learner to be robust and have good performance even with small training data. Ensemble of ensembles method combining different ways of combining models improves performance upon ensemble models, and generally has the best performance.
We conclude that ensemble models, if properly applied, are able to improve model performance. We posit that transport modeling can benefit enormously from the wider adoption, and awareness of ensemble forecasting methods. We hope that this research opens the door to methodically adopting ensemble models into transport modeling, that future transport research can build upon.
The first journal article published from the dissertation is:
- Wu, Hao, and Levinson, D. (2021) The Ensemble Approach to Forecasting: A Review and Synthesis. Transportation Research part C. Volume 132, 103357 [doi]