| Summary: | Motivated by the prevalence of uncertainty and the widespread use of modeling in Transportation, we develop and study effective methods for modeling Transportation under uncertainty. These methods are Machine Learning-based, i.e., they extract patterns from data and leverage them for better modeling. We study them through several case studies, including: quick adaptation of traffic models upon road incidents; estimation of mobility demand from limited observations; and predictive optimization of dynamic Public Transport. Our results yield several positive conclusions about the effectiveness of the studied methods for current and future Transportation.
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