DEVELOPMENT OF AN INTEGRATED URBAN MODELLING FRAMEWORK TO EXAMINE IMPACTS OF COVID-19 ON TRANSPORT AND LAND-USE SYSTEMS

Abstract

This thesis develops an integrated urban modelling framework (IUMF) to assess the short-term, medium-term and long-term impacts of COVID-19 on transport and land-use systems. It starts with analyzing public discourses in Twitter using data mining techniques to better understand the impacts of COVID-19 on transport modes and mobility behavior. Then, it advances Bayesian networks based modelling approaches to examine post-pandemic mobility choices of individuals utilizing a questionnaire survey in Halifax, Canada. To explore long-term impacts, the thesis first applies an existing integrated urban model simulating residential location and mobility tool ownership for the next 10 years (2021-2030). Finally, it develops a novel framework by coupling longer-term decisions models within the IUMF that enables to simulate individuals’ residential location and mobility behavior in response to the pandemic. Developed tools can be used in case of future pandemics or any other mobility disruptive events for transport and land-use impact assessments.