COVID has exacerbated two emerging trends in transportation analysis: (1) the rise of passively-generated big data; and (2) the increasing need to deal with the “unexpected” disruptions. This talk emphasizes the need for learning big and small data for transportation planning and resilience analysis. Different ways of learning are described, with applications ranging from long-term planning analysis to rapid responses under disruptions.
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Research Areas
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This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision. |
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The webinar will discuss the U.S. DOT Intersection Safety Challenge Prize Competition, including a program overview, the Prize Competition structure, and Stage 1A expectations. |
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In this talk, Professor Fosgerau presents results from application to large datasets (1,337,096 GPS traces of car trips, 280,000 GPS traces of bicycle trips) in Copenhagen. |
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