August 18, 2023
Conventional urban data collection methods do not account for the behavioural and psychological experiences of the population. Therefore, little is known about the health and well-being of vulnerable citizens that rely on public services such as transit during disruptive events including COVID-19. On-going work at UPAL is developing machine learning approaches to augment conventional data collection to inform mobility, accessibility and infrastructure planning.
Monitoring the well-being of vulnerable transit riders using machine learning based sentiment analysis and social media: Lessons from COVID-19
M Tran, C Draeger, X Wang, A Nikbakht Environment and Planning B: Urban Analytics and City Science 50 (1), 60-75