  The recovery of the public transportation system is critical for both social
re-engagement and economic rebooting after the shutdown during pandemic like
COVID-19. In this study, we focus on the integrated optimization of service
line reopening plan and timetable design. We model the transit system as a
space-time network. In this network, the number of passengers on each vehicle
at the same time can be represented by arc flow. We then apply a simplified
spatial compartmental model of epidemic (SCME) to each vehicle and platform to
model the spread of pandemic in the system as our objective, and calculate the
optimal open plan and timetable. We demonstrate that this optimization problem
can be decomposed into a simple integer programming and a linear
multi-commodity network flow problem using Lagrangian relaxation techniques.
Finally, we test the proposed model using real-world data from the Bay Area
Rapid Transit (BART) and give some useful suggestions to system managers.
