Transit Agencies struggle to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints. We have been working for the last several years to design AI-based scheduling systems to solve the problem of allocating vehicles and drivers to transit services, scheduling vehicle maintenance, and electric-vehicle charging, proactive stationing, and dispatch of vehicles for fixed-line service to mitigate unscheduled maintenance and unmet transit demand, aggregating on-demand transit requests, and dispatching and routing on-demand vehicles. Similar to the incident response, the decision support systems face key challenges: environments are non-stationary and difficult to predict due to human factors and complex processes affecting transit demand and traffic as well as unscheduled maintenance and accidents; simulations are expensive and complex as city-scale simulations need to consider millions of individuals and vehicles.