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Expert with Clustering: Hierarchical Online Preference Learning Framework
Even more so than the typical recommendation system, it is crucial to minimize regret, because 1) the mobility options directly affect the lives of the users, and 2) the system sustainability relies on sufficient user participation.
Incentive Design for Eco-driving in Urban Transportation Networks
Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks.
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
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