MaREI at UCC and Columbia University develop new method to improve climate policy

• MaREI at UCC researchers introduce an innovative framework to project transport demands for regions having multiple countries spanning different socio-economic development pathways.
• The research increases the accuracy of estimating future demands for passenger and freight transport that collectively account for 20% of global greenhouse gas emissions.
• Reducing transport related emissions remains a considerable challenge for climate policy.

Researchers at University College Cork (UCC) and Columbia University have developed new research that will improve the accuracy of estimating future demands for passenger and freight transport, that collectively account for 20 % of global greenhouse gas emissions.

The United Nations estimates that the global population could grow from 7.7 billion people worldwide in 2019 to around 9.7 billion in 2050. The additional population and economic growth will likely lead to increased demand for transport services.

Reducing transport related emissions remains a considerable challenge for climate policy. Until now, transport demand projection tasks were handled by simulating demands or by using regression-based analysis. Now through this UCC and Columbia research countries across the world will be more accurately be able to estimate future transport demands.

This research, published in Scientific Reports,  introduces a new innovative machine learning approach called TrebuNet. The results demonstrate that this new TrebuNet architecture achieves superior performance compared with both traditional regression methods and more recent state of the art neural network and machine learning methods. The improvements extend to regional demand projection for all modes of transport demands at short, decadal, and medium-term time horizons. 

Siddharth Joshi, who led this research as part of his PhD in Energy Engineering at UCC commented  “This study provides insights into development of a novel machine learning architecture that increases the accuracy in the estimation of transport energy service demands. The innovative machine learning architecture and its benefits are measurable for the energy modelling community and is transferable to different disciplines.”

“Not only are the accurate transport demand projections important for energy system models and climate policy, but they also act as backbone for understanding the future direction of global energy markets” stated Brian Ó Gallachóir, UCC Professor of Energy Engineering.