AI and transport sector
Scholars at University College Cork and Columbia University have invented a new method of improving the precision of evaluating future demands of passengers and heavy-load transport.
According to the UN, the world population could grow to around 10 billion by 2050. This will lead to increased demand for transport services.
Downsizing transport emissions is a big challenge for climate policy. Before this moment professionals addressed simulating demands models or regression-based analysis. Thanks to the new method, countries world-wide will have more knowledge in projecting future transport needs.
The new approach is called TrebuNet.It shows superior results performance-wise comparing to traditional regression methods, neural network and machine learning. The method relates to regional demand projection for different time periods and for a variety, if not all transport needs.
Siddarth Joshi says, that the brand new machine learning method increases exactness in evaluation of transport energy service needs. The method may be used for energy modeling community and other areas.
Brian O Gallachor says that, the method is suitable for energy system modeling, climate policy, future direction of global energy markets.
James Glynn adds, that the system is good for energy systems modelling, data analysis and filling the gaps in the field in general, particularly decarbonization.
Climate action in needed when it comes to net-zero of transport decarbonization by 2050. So, collaboration between Columbia SIPA and UCC opens new possibilities for energy systems modeling, data science for hard-data based research for decision makers to make climate policy change.
AI Catalog's chief editor
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