NEXt generation activity and travel behavioUr modelS: Bringing together choice modelling, ubiquitous computing and data science

The mobility landscape is undergoing rapid changes with the advent of new technologies and the growing complexities of travel patterns. NEXUS focuses on developing next-generation mathematical models of travel behaviour that can better predict the activity and travel decisions in this changing landscape. This is being achieved by developing new frameworks that bring together Choice Modelling (CM), Ubiquitous computing (UC) and Machine Learning (ML) techniques to utilise passively generated real-world mobility traces (from public transport smart cards, mobile phones, etc.), neurophysiological signals and virtual reality (VR) to model decision making in future scenarios.
The research focus for the 1st phase has been to model day-to-day activity and travel decisions (choice of travel mode, destination, time-of-travel, etc.) - in the context of current and emerging modes (self-driving cars, air taxis, hyperloops) and in regular and challenging environments (e.g. pandemic, economic and social unrests). In the next phase, I plan to focus on extending the theme of fusing different types of data and bridging CM, UC and ML in the context of emergency situations - during natural disasters and acute transport network disruptions, in particular.
The limitations of the current behaviour models for emergency situations (e.g., whether or not to evacuate, when to depart, which mode and route to take, etc.) arise from multiple factors. Firstly, they assume travel choices in such situations are based on rational decision-making principles which is often not the case. Rather, the choice alternatives in such scenarios have varying levels of uncertainty and the decisions very often are impulsive and based on 'gut feeling'. Secondly, the current models do not account for the myriad of psychological factors that could influence an individual's decision in such difficult situations, for example, the risk-taking propensity, the perceived effect of stress or the thinking process in...