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Researchers at the Future Urban Mobility (FM) interdisciplinary research group atSingapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, have created a synthetic framework known as theory-based residual neural network (TB-ResNet), which combines discrete choice models (DCMs) and deep neural networks (DNNs), also known as deep learning, to improve individual decision-making analysis used in travel behavior research.
Transportation Research: Part B, SMART researchers explain their developed TB-ResNet framework and demonstrate the strength of combining the DCMs and DNNs methods, proving that they are highly complementary.
As machine learning is increasingly used in the field of transportation, the two disparate research concepts, DCMs and DNNs, have long been viewed as conflicting methods of research.