Transit Networks, Social Contacts, and Open Data Meet Public Transportation Plans for Post-COVID-19: A Canadian Case Study

Abstract

The emergence of COVID-19 and its variants has dramatically shifted the way that societies respond to a pandemic crisis and postpandemic plans. One of the response needs is the ability to track potential transmissions within population movements effectively, which can exploit the means of pervasive computing by collecting and processing ubiquitously acquired anonymously aggregated data, as well as long-term data that constitute prior but limited contextual knowledge. This article presents a practical method for the assessment of the risk profiles of communities by acquiring, fusing, and analyzing data from public transportation, district population distribution, passenger interactions, and cross-locality travel data while still preserving individual user privacy. The proposed framework fuses these data sources into a realistic simulation of a transit network for a given time span to report on the risk of the public transit system as whole, as well as problematic wards and routes. By shedding credible insights into the impact of public transit on pandemic spread, the article findings will help to set the groundwork for aggregate transmission simulation tools that could provide pandemic response teams with a robust framework for the evaluations of city districts most at risk, and how to adjust municipal services accordingly.