Using Artificial Neural Networks to Enhance Travel Behavior Research
The Singapore-MIT Alliance for Research and Technology combined discrete choice models and deep learning to form theory-based residual neural network. The breakthrough can improve urban planning and transportation as it improves decision-making analysis used in travel research behavior.
Urban Mobility Lab at MIT postdoc and lead author Shenhao Wang says, “Improved insights to how travelers make decisions about travel mode, destination, departure time, and planning of activities are crucial to urban transport planning for governments and transport companies worldwide. I look forward to further developing TB-ResNet and its applications for transport planning now that it has been acknowledged by the transport research community.”
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