This paper presents a two-stage stochastic program that reoptimizes multimodal transit schedules citywide. The model works by perturbing or offsetting the schedule such that the expected value of waiting times at all transfer points in the system is minimized. Probabilistic information on transfers is gathered from a prototypical journey planner, a public-facing tool that transit riders query to find optimal paths through a multi-modal network. Aggregating journey plans in this manner provides information on optimal transfers as perceived by the service operator, which are then targeted for improvements. The model is implemented on the large-scale transit network of Washington, D.C., where sampled journey plans representing 9% of the daily transit demand are used to generate a modified schedule that leads to a reduction in passenger wait times by 26.38%. The results serve to demonstrate how operators can take a user-centric view of their system as a fabric of services, gain insights from user interaction, and achieve no-cost improvements from coordinating services while accounting for uncertainty.
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