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.

Domestic transport is the UK's highest emission sector, and congestion in cities is costly (e.g. London £5.1bn in 2021). Drastically reducing urban car dominance is imperative to reach the UK's 2050 net-zero target, but also an unparalleled opportunity to create more equitable, inclusive and accessible cities of the future across the country. Recent UK investments of approximately £15bn seek to radically transform urban mobility and modality: £2bn for half of urban journeys to be cycled/walked by 2030 (e.g., cycle lanes, mini-Holland schemes), £5.7bn City Region Sustainable Transport Settlements (e.g., Manchester bus and cycle schemes), and £7bn to level up local bus services. To realise full investment potential, and develop holistic adoption pathways towards net-zero, inclusive mobility, multimodal transport must be effectively planned, managed and operated, with people and their differences as a core consideration. This is challenging for a complex system-of-systems. On the supply side, modes compete for limited road space on shared infrastructure, creating conflicts. On the demand side, modes complement each other in intermodal journeys, jointly influencing uptake. For example, cycle lanes promote cycling, but may impact road speeds and exacerbate congestion and pollution, highlighting the need to evaluate person-level mobility and system-level emissions. A recent survey reported two-thirds of disabled respondents finding cycling easier than walking, highlighting the need to consider the broad disability spectrum and the potential for cycle lanes to improve access for all. Therefore, holistically optimising cycle lane schemes, as with all multimodal schemes, requires integrated methodologies: fully capturing multimodal transport systems' distributed and interconnected processes, the complexities of modal competition and complementarity, and the heterogeneity of traffic and population. My research will overcome these research challenges and develop the first multiscale digital twin for the transport-people-emission nexus using a truly integrated approach to model and simulate multimodal urban transport, advancing and coalescing my adventurous research in multimodality, using traffic flow theory, agent-based modelling, and machine learning. This will enable the development of holistic adoption pathways towards net-zero, inclusive mobility through scenario testing and optimisation, with guidance and recommendations to support implementation. Leading a strong consortium of 3 cities and 12 partners, covering the entire multimodal transport value chain, I will collaboratively exploit the digital twin to realise UK strategic agendas: net-zero; Equity, Diversity and Inclusivity (EDI); and levelling-up. By holistically enhancing mobility for everyone, my Fellowship also will propel the Green Revolution for economic growth, leveraging the net-zero mission to unlock new business opportunities, and establish the UK as a global leader in digital technologies to tackle climate change. I will deliver a strong positive impact on making net-zero a net win for people, industry, the UK, and the planet, thereby enabling both me and the UK to become world leaders in multimodal urban transport, at the forefront of research and innovation.

The impact of road traffic on local air quality is a major public policy concern and has stimulated a substantial body of research aimed at improving underlying vehicle and traffic management technologies and informing public policy action. Recent work has begun to exploit the capability of a variety of vehicle-based, person-based and infrastructure-based sensor systems to collect real time data on important aspects of driver and traffic behaviour, vehicle emissions, pollutant dispersion, concentration and human exposure. The variety, pervasiveness and scale of these sensor data will increase significantly in the future as a result of technological developments that will enable sensors to become cheaper, smaller and lower in power consumption. This will open up enormous opportunities to improve our understanding of urban air pollution and hence improve urban air quality. However, handing the vast quantities of real time data that will be generated by these sensors will be a formidable task and will require the application of advanced forms computing, communication and positioning technologies and the development of ways of combining and interpreting many different forms of data. Technologies developed in EPSRC's e-Science research programme offer many of the tools necessary to meet these challenges. The aim of the PMESG project is to take these tools and by extending them where necessary in appropriate ways develop and demonstrate practical applications of e-Science technologies to enable researchers and practitioners to coherently combine data from disparate environmental sensors and to develop models that could lead to improved urban air quality. The PMESG project is led by Imperial College London, and comprises a consortium of partners drawn from the Universities of Cambridge, Southampton, Newcastle and Leeds who will work closely with one another and with a number of major industrial partners and local authorities. Real applications will be carried out in London, Cambridge, Gateshead and Leicester which will build on the Universities' existing collaborative arrangements with the relevant local authorities in each site and will draw on substantial existing data resources, sensor networks and ongoing EPSRC and industrially funded research activities. These applications will address important problems that to date have been difficult or impossible for scientists and engineers working is this area of approach, due to a lack or relevant data. These problems are of three main types; (i) measuring human exposure to pollutants, (ii) the validation of various detailed models of traffic behaviour and pollutant emission and dispersion and (iii) the development of transport network management and control strategies that take account not just of traffic but also air quality impacts. The various case studies will look at different aspects of these questions and use a variety of different types of sensor systems to do so. In particular, the existing sensor networks in each city will be enhanced by the selective deployment of a number of new sensor types (both roadside and on-vehicle/person) to increase the diversity of sensor inputs. The e-Science technologies will be highly general in nature meaning that will have applications not only in transport and air quality management but also in many other fields that generate large volume of real time location-specific sensor data.Each institution participating in this project will be submitting their resource summary individually to Je-s. The resources listed within this Je-S Proposal are solely those of Imperial College with other institutions submitting their costs seperately, with one case for support.

We will mount sensors on pedestrians and cyclists to monitor their exposure to pollution from transport. This will be an addition to the TIME-EACM project, which is about to use Cambridge City as a test bed for a variety of ways to gather data about traffic flow, and is writing middleware to analyse the data in real time.The initial part of the study will be to confront the technical challenges associated with sensors that need to be highly portable. Sensor technologies are now advancing to the point where parts per billion sensitivities are becoming achievable in small low power devices for species relevant to local air quality including ozone, nitrogen dioxide and a range of hydrocarbons. The challenge will be to link such sensors to effective mobile systems to broadcast data back to central points for analysis and presentation, and to locate their wearers sufficiently accurately. The TIME-EACM project will log and store data and integrate databases with information flow from its sensors, and the data stream from the pervasive environmental sensors will be added to this. The TIME-EACM middleware will be compatible with data on pollution from pervasive environmental sensors. All data will be time-stamped and location-stamped and correlated with TIME-EACM data on traffic flow.

The impact of road traffic on local air quality is a major public policy concern and has stimulated a substantial body of researchaimed at improving underlying vehicle and traffic management technologies and informing public policy action. Recent work hassought to use a variety of vehicle-based, person-based and infrastructure-based sensor systems to collect data on key aspects ofdriver and traffic behaviour, emissions, pollutant concentrations and exposure. The variety and pervasiveness of the sensor inputsavailable will increase significantly in the future as a result both of the increasingly widespread penetration of existingtechnologies (e.g., GPS based vehicle tracking, CANbus interfaces to on-board engine management system data) within thevehicle parc and the introduction of new technologies (such as e.g., UV sensing and nanotechnology based micro sensors). Aparticularly exciting direction for future development will be in the use of vehicles as platforms for outward facing environmentalsensor systems, allowing vehicles to operate as mobile environmental probes, providing radically improved capability for thedetection and monitoring of environmental pollutants and hazardous materials.However, these developments present new and formidable research challenges arising from the need to transmit,integrate, model and interpret vast quantities of highly diverse (spatially and temporally varying) sensor data. Our approach in thisproject is to address these challenges by novel combination and extension of state-of-the-art eScience, sensor, positioning andmodelling (data fusion, traffic, transport, emissions, dispersion) technologies. By so doing, we aim to develop the capability tomeasure, model and predict a wide range of environmental pollutants and hazards (both transport related and otherwise) using agrid of pervasive roadside and vehicle-mounted sensors. This work will be at the leading edge of eScience, stretching thecapabilities of the grid in a number of aspects of the processing of massive volumes of sensor data.