COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. This project proposed a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19, including social distancing and working from home. Open-source agent-based simulation models were used to evaluate transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation was used to evaluate the emissions impacts. Finally, system-wide contact exposure on the subway was estimated from the traffic simulation output. The social distancing requirement in public transit was found to be effective in reducing contact exposure, but it had negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the post-COVID negative impacts were higher due to behavioral inertia and social distancing. The findings suggested that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are following. The proposed integrated traffic simulation models and emission estimation model can help policy makers evaluate the impact of policies on traffic congestion and emissions, as well as identify hot spots, both temporally and spatially.