Transit networks, social contacts and open data meet public transportation plans for post-COVID-19: A Canadian case study

This article presents a practical method for the assessment of the risk profiles of communities by tracking / acquiring, fusing and analyzing data from public transportation, district population distribution, passenger interactions and cross-locality travel data. The proposed framework fuses these data sources into a realistic simulation of a transit network for a given time span. By shedding credible insights into the impact of public transit on pandemic spread, the research findings will help to set the groundwork for tools that could provide pandemic response teams and municipalities with a robust framework for the evaluations of city districts most at risk, and how to adjust municipal services accordingly.