The COVID-19 pandemic has transformed all aspects of life in a very short time, including how and when we travel. All transportation modes, including driving, walking, and cycling, have been impacted by the spread of disease to different extents. Public transit may have been impacted most, given that passenger density--the key to efficient transit service--is in direct conflict with physical distancing principles. While mobility is expected to recover in due time, it is difficult to predict the long term effects of the pandemic on travel behavior and preferences. Even when the virus is no longer a threat to public health, residual fears and new habits formed during the pandemic may affect activity choices, destination choices, and mode choices. These decisions, in turn, affect energy consumption by the transportation sector.

In this project, we investigate behavioral and preference changes during and in the medium term after COVID-19 across three case study cities: Singapore, Chicago, and Boston. We further analyze these changes with respect to sociodemographic data, built environment data, and different policy responses in each case study city to better understand how COVID-19's impact on mobility differed by social group, and what operational strategies, infrastructure, and equipment changes may help them recover. To accomplish these research goals, we leverage our exclusive access to high-resolution trip records from public transit smart card data and TNC trip data as well as longitudinal panel surveys of transit riders designed for this project.

This project is funded by the Mobility Systems Center, an MIT Energy Inititaive Low Carbon Energy Center, and the Barr Foundation.