The COVID-19 pandemic has not only disrupted the lives of millions but also created exigent operational and scheduling challenges for public transit agencies. Agencies are struggling to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints due to social distancing, and reduced services due to driver unavailability. A number of transit agencies have also begun to help the local food banks deliver food to shelters, which further strains the available resources if not planned optimally. At the same time, the lack of situational information is creating a challenge for riders who need to understand what seating is available on the vehicles to ensure sufficient distancing. In partnership with the transit agencies of Chattanooga, TN, and Nashville, TN, and Prof. Aron Lazka, University of Houston we are rapidly developing integrated transit operational optimization algorithms, which will provide proactive scheduling and allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). A key component of the research is the design of privacy-preserving camera-based ridership detection methods that can help provide commuters with real-time information on available seats considering social-distancing constraints.