How to Evaluate and Minimize the Risk of COVID-19 Transmission within Public Transportation Systems
Research Team: Zuojun Max Shen (lead) and Yiduo Huang
UC Campus(es): UC Berkeley
Problem Statement: In response to the COVID-19 pandemic, local governments and public transportation agencies implemented various policies during the recovery phase to control the spread of infection, such as reducing service frequency and reducing network coverage. However, it is not entirely clear which policies are needed, and the extent to which they should be implemented. Policies should prevent COVID-19 transmission, comply with social distancing rules, promote safe travel, and be financially feasible. The infection risk of COVID-19 is not considered in pre-pandemic network planning and scheduling methods, which focus on travel time or budget reduction. Furthermore, safety measures such as social distancing and vehicle disinfection need to be considered when drafting network reopening plans and new schedules for public transportation under COVID-19.
Project Description: Taking social distancing orders and the risk of new infections into consideration, the research team addressed the following problems simultaneously in this research project: (1) Network reopening design: how to determine which lines and stations to open in a given region; (2) Timetable design: how to design a timetable to satisfy travel demand and minimize the risk of new COVID-19 cases; (3). Policy evaluation: What is the risk of COVID-19 transmission given an existing timetable and reopening plan?This study summarizes previous models and proposes a general model framework for evaluating COVID-19 risks in public transportation systems which consists of combining two models: (1) a contact tracing model, using a space-time passenger flow network to estimate the contact and travel history of each passenger, and (2) a pandemic mechanism using a spatial compartmental model used in epidemiology, where the results from the contact tracing model are used to predict the estimated number of new infections.