Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
This framework solves transit network design by accounting for two types of demand uncertainty: 'core' riders who must use transit and 'latent' riders who might adopt it based on service quality. It uses machine learning to predict these uncertainties and constraint programming to find the best network design that maximizes total expected coverage within budget constraints.