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