  The COVID-19 pandemic has severely affected many aspects of people's daily
lives. While many countries are in a re-opening stage, some effects of the
pandemic on people's behaviors are expected to last much longer, including how
they choose between different transport options. Experts predict considerably
delayed recovery of the public transport options, as people try to avoid
crowded places. In turn, significant increases in traffic congestion are
expected, since people are likely to prefer using their own vehicles or taxis
as opposed to riskier and more crowded options such as the railway. In this
paper, we propose to use financial incentives to set the tradeoff between risk
of infection and congestion to achieve safe and efficient transportation
networks. To this end, we formulate a network optimization problem to optimize
taxi fares. For our framework to be useful in various cities and times of the
day without much designer effort, we also propose a data-driven approach to
learn human preferences about transport options, which is then used in our taxi
fare optimization. Our user studies and simulation experiments show our
framework is able to minimize congestion and risk of infection.
