Modelling residential relocation behaviour combining passive revealed preference data and stated preference survey data

Understanding how various factors shape residential relocation is crucial for effective infrastructure planning and policy. Yet, existing revealed preference (RP) datasets often lack essential demographic or dwelling details, while stated preference (SP) surveys are prone to hypothetical bias and behavioural incongruence. To fill in this gap, this study presents a residential relocation choice model that combines residential location data derived from passively generated public transport smart cards of 82,720,872 users and SP data from 971 respondents (8,739 observations) in Beijing, China. Both types of data were generated or collected in the backdrop of the COVID-19 pandemic, which led to higher-than-usual residential relocations in Beijing. The integrated approach, which accounts for the scale difference between the two datasets, reveals a strong preference for city-centre locations. But higher infection risks increase the likelihood of moving away from crowded areas, whereas flexible work-from-home policies lower the inclination to relocate to the centre. These findings quantify how different pandemic-related factors alter traditional relocation drivers. The results can guide policymakers in designing more resilient housing and transport policies, especially under future disruptions like pandemics. Moreover, the data-fusion framework offers a replicable strategy for researchers and planners seeking to capture both real-world behaviours and hypothetical scenarios in residential location studies. • The study combines passively collected public transport smart card data (RP) with stated preference (SP) survey data from Beijing, using a joint modelling framework. • Combining RP and SP data mitigates the limitations of each data source and enhances the reliability of residential relocation behaviour analysis. • The research applies the Push-Pull-Mooring (PPM) model to interpret residential relocation dynamics and uses pandemic-related factors such as infection risk, remote work flexibility as mooring factors. • Results show a continued preference for central city locations, but this is counteracted by higher infection risks and increased feasibility of remote work. • The insights offer actionable guidance for urban planners and policymakers to develop more resilient housing and transport strategies, particularly in preparing for future disruptions like pandemics.