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Creators/Authors contains: "Anusha Neupane, Venktesh Pandey"

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  1. (, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) Undergraduate Consortium)
    Predictive routing is effective in knowledge transfer. However, it ignores information gained from probability distributions with more than one peak. We introduce traffic multimodal information learning, a new class of transportation decision-making models that can learn and transfer online information from multiple simultaneous observations of a probability distribution with multiple peaks or multiple outcome variables from one time stage to the next. Multimodal learning improves the scientific and engineering value of autonomous vehicles by determining the best routes based on the intended level of exploration, risk, and limits. 
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    Accepted Manuscript Full Text Available