Martino Tran

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 

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