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

In many countries around the world, the transport sector claims a major share of the public spending. For example, the total public spending on transport in the UK was £22.5 billion in 2018. The potential impacts of new transport decisions can be evaluated using mathematical models to predict what people will do, when and where, and how they will travel in-between different locations in any given scenario. These travel behaviour models are typically based on theories of economics and psychology and developed using survey data. However, new forms of mobility (e.g. self-driving cars, Uber, shared-bikes) and new types of users (e.g. older travellers, migrants) are leading to radical changes in the mobility landscape. The traditional data and models are failing to deal with the rising complexities of activity and travel patterns which motivates NEXUS.
The limitations of the current mainstream models arise from multiple factors. Firstly, they assume travel behaviour is solely based on the age, income, attitudes, etc. of the traveller and the attributes of the alternatives (e.g. travel times, costs). They do not account for the myriad of psychological factors that could influence an individual's decision, for example, the effect of stress, fatigue or the 'thinking process' more generally. Secondly, the data used for developing the models typically rely on small-scale surveys where travellers are asked to report/log their past behaviour or to state their choices based on descriptions of hypothetical scenarios, which very often are not reliable measures of the real-world travel behaviour.
On a parallel stream, large amounts of mobility data are constantly generated from sources like GPS, mobile phones and social media. Advanced technologies and machine learning (ML) methods have also made it possible to measure the 'mental state' of the travellers by simple wristbands, discrete clip-ons and smartphone-based sensors and infer their thinking processes from brain imaging. Further, advances in virtual reality (VR) technology has made it possible to immerse travellers in future scenarios to obtain more realistic responses. Bringing together new data and methodologies can lead to a step change in travel behaviour modelling - but the framework to unify these different streams of research is yet to be formulated.
NEXUS proposes to address this research gap by developing methodologies to augment travel behaviour models with novel forms of data. These will include: (a) real-world mobility data generated from GPS, mobile phones and other passive sources; (b) dynamic data about the 'state-of-the-mind' measured using sensors; and (c) experimental data on travel behaviour from VR settings of hypothetical future scenarios. Utilizing passive mobility data and sensing mental states will involve utilizing state-of-the-art ML and ubiquitous computing techniques. Combining the different types of real-world and experimental data sources for predicting behaviour in new scenarios will involve integrating these in traditional travel behaviour modelling framework.
Merging these techniques, for the very first time outside the lab-setting, will produce a richer set of travel behaviour models that can better deal with radically different transport scenarios and user-groups in the future. The models will be implemented in a microsimulation platform to simulate the mobility behaviour in different policy scenarios with increased accuracy and aid the planners and policy-makers in making more informed investment decisions.
This multi-disciplinary research will build on and extend my past experience in behavioural modelling using big data and sensors. It will support my transition to a research leadership role at the University of Leeds and collaboration with globally renowned academics in transport, psychology and computing. Partnership with non-academic partners will ensure the quick transition of the research to practice and real-world impact.

The anticipated impacts beyond the academia are as follows:
Transport and urban planning
Better activity and travel behaviour models will have a direct impact on the state-of-the-art transport and urban planning and hence contribute to ensure better use of public funds, both in the UK and beyond. For example, the total public spending on transport in the UK in 2018 was £22.5 billion. However, these investments are primarily driven by the WebTAG guidance based on Value of Travel Time Savings derived from stated-preference (SP) surveys which has well-known limitations like behavioural incongruence and hypothetical bias, as well as small sample sizes and infrequent update frequencies (the last study was conducted after a 12-year gap). This highlights the importance of the proposed research, which will make it possible to better utilize passive data sources in mainstream modelling and appraisal. Further, behaviour models developed from the collected from VirtuoCITY, a VR suite of world-leading urban simulation technologies housed at the University of Leeds, will enable better insights about future mobility decisions compared to traditional SP surveys. Embedding the behavioural models in microsimulation platforms will make it possible to get an easy to comprehend policy-analyses suite which can be used to investigate what will be the activity and mode use patterns, travel frequencies, distances, time-use in presence of different forms of MaaS, different penetration rates of automated vehicles, elevated buses, flying taxis etc. and how they may vary with different user compositions. All these will enable planners to make better decisions and deliver transport options that are better suited for the user needs - ultimately leading to optimised allocation of resources, reduction in congestion, energy and emissions and benefit public. The carefully chosen partners UK Department for Transport (DfT) and Asian Development Bank (ADB) will help me to achieve this impact
Data analytics
The developed methodologies will enable both industry and commercial entities to get value out of the passively collected data for other purposes. Examples include using mobile phone data for better modelling migration and evacuation decisions, etc. For instance, the industrial partner Citi Logik has recently conducted studies on existing cross-border movements between Ireland and Northern Ireland based on mobile phone data which, though very useful for present analysis, may be radically different after Brexit. Methodologies developed in NEXUS can identify the relative impacts of different drivers of mobility behaviour and help to make better predictions about movements beyond Brexit. Techniques to utilize these otherwise wasted resources have commercial values as well as societal benefit. Partnership with Citi Logik, specializing in getting better value out of mobile phone data in the UK, will help me to make an impact in this area.
Global South
The data issues for modelling are scarcer in the countries of the global south where the transport investments have an even larger share of the economic investments. Partnership with ADB, which conducts projects in 47 countries in Asia, as well as InTALINC - the network of transport researchers in Bangladesh, Nigeria, Uganda and Ghana formed out of the GCRF networking grant I co-lead will help me to make impact in this area.
Commercial microsimulation
There is a growing interest among commercial simulation developers to better capture the sources of heterogeneity of the travellers in the simulation tools. The developed models will help in improving the fidelity of the commercial microsimulation tools by addressing this research need. Having PTV as a non-academic partner will help in better understanding of the needs of the commercial microsimulation community and expedite the technology transfer. The developed models will be open-sourced as much as possible to maximize the transition to the commercial simulatio