Explore a machine learning-based metaheuristic approach for efficiently reoptimizing autonomous ridesharing plans in dynamic environments. Delve into the local search-based metaheuristic that utilizes destroy-repair operators selected through machine learning, trained on over 1.5 million examples of solved ridesharing subproblems. Examine computational experiments conducted on dynamic instances from Uber Technologies Inc. data, showcasing the proposed approach's 9% average performance improvement over benchmark data-driven metaheuristics. Gain insights into the correlation between vehicle routing features and metaheuristic performance in autonomous ridesharing operations, presented by Claudia Bongiovanni from HEC Montréal in this 27-minute DS4DM Coffee Talk at GERAD Research Center.