Addressing challenges in utilising smartphone-based travel survey data using choice modelling and machine learning techniques

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The Covid-19 pandemic has led to substantial changes in our activity and lifestyle preferences and triggered the need to rethink the design of cities. This requires sound understanding of the factors influencing our long, medium and short-term mobility needs (residential location, vehicle ownership and mode-switching behaviour for instance). The project aims to address this research gap by developing mathematical models to predict mobility decisions in different stages of the pandemic as well as in the post-pandemic world. The outputs are intended to enable urban planners and transport providers to quickly respond to changed mobility requirements in the event of a new pandemic. Behavioural responses in the event of a pandemic are complex and heterogeneous. The proposed research hence requires investigating alternative behavioural theories to enrich the traditional activity and mobility models. It also requires extensive data for model estimation. The project proposes to combine survey data with passively generated smart-phone app location data to calibrate these models. The methodological nature of the proposed research and its focus on big data sources makes it particularly suited for the AQM. The proposed methodological work on data fusion will have applications beyond transport.