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Commuting is an important part of daily life. With the gradual recovery fromCOVID-19 and more people returning to work from the office, the transmission ofCOVID-19 during commuting becomes a concern. Recent emerging mobility services(such as ride-hailing and bike-sharing) further deteriorate the infection risksdue to shared vehicles or spaces during travel. Hence, it is important toquantify the infection risks in commuting. This paper proposes a probabilisticframework to estimate the risk of infection during an individual's commuteconsidering different travel modes, including public transit, ride-share, bike,and walking. The objective is to evaluate the probability of infection as wellas the estimation errors (i.e., uncertainty quantification) given theorigin-destination (OD), departure time, and travel mode. We first define ageneral trip planning function to generate trip trajectories and probabilitiesof choosing different paths according to the OD, departure time, and travelmode. Then, we consider two channels of infections: 1) infection by closecontact and 2) infection by touching surfaces. The infection risks arecalculated on a trip segment basis. Different sources of data (such as smartcard data, travel surveys, and population data) are used to estimate thepotential interactions between the individual and the infectious environment.The model is implemented in the MIT community as a case study. We evaluate thecommute infection risks for employees and students. Results show that most ofthe individuals have an infection probability close to zero. The maximuminfection probability is around 0.8%, implying that the probability of gettinginfected during the commuting process is low. Individuals with larger traveldistances, traveling in transit, and traveling during peak hours are morelikely to get infected.

Modeling Virus Transmission Risks in Commuting with Emerging Mobility Services: A Case Study of COVID-19