An efficient transportation system is vital to the economic and social well-being of large cities. The transport demand implied by economic growth, however, requires transport networks to become more and more complex, making their management difficult. Fortunately, modern systems such as the London Underground generate vast amounts of data that can be analysed to better understand passenger behaviour and needs. Besides understanding the typical daily patterns that we can observe on a regular basis, Data Science methods allows us to look into in the less usual events such as unplanned disruptions that are still important to any user, and to also model individualised behaviour instead of only aggregates. In a large system such as the London Underground, signal failures and disruptive events eventually take place, requiring passengers to change plans in a variety of ways. This research provides advanced statistical modelling and machine learning approaches to learn from past events to examine how passengers adapt themselves when a disruption occurs. When a disruption takes place, the model will provide information of likely changes, such as increased number of passengers leaving a station because they could not reach their destination. These models are important for transport authorities to understand the resilience of the system, different combinations of location and time of a disruption, and unusual responses from passengers that may motivate different communication strategies to inform users of better travel adjustments. This research also opens up conceptual ideas to be exploited in the future using new technologies to monitor and adaptively respond to passenger needs in a more optimised and time-effective way.

Many guidelines are hard to follow because they involve regulating largely unconscious behaviours. Despite a general willingness to comply with official guidelines, it is hard for people to do so. Take the recommendation to avoid touching one's face: The behaviour can transmit virus from one's hands to one's eyes, nose, or mouth, which in turn can cause self-infection. Unfortunately, face touching is habitual, and evidence shows that when people are made aware of touching their face, they do it more. This project aims to assess the effects of public awareness campaigns aimed at helping people to reduce the rate at which they touch their face. It also pays attention to the polarised environment in which such campaigns are fielded by focusing on the messenger behind the campaigns. In a randomized controlled trial, it will investigate the effectiveness of these messages and test hypotheses related to how different messengers and whether a message becomes politicised affect observance with the advice. Specifically, it will assign survey participants to a combination of a messenger and a poster either designed to raise awareness of Covid-19 or designed to raise awareness of Covid and move attention to reducing face touching frequency by keeping hands occupied. The study outcomes will be a behavioural outcome for face touching frequency in recorded video responses, planned observance to guidelines, and message credibility.

The concept of the 15-minute city was developed for inner city districts in major metropolitan centres, but it is also highly relevant to outer metropolitan areas and smaller towns and cities to foster more sustainable travel behaviour. Yet, the latter areas often have additional sustainability challenges including greater car dependence, struggling local retail centres, disintegrated housing developments and weaker active travel infrastructure. The ENHANCE project is designed to understand the barriers for the application of 15mC principles in outer metropolitan areas and small cities and provide evidence to improve local planning decision-making. This will be achieved through three main tasks: 1. Creating multi-modal composite accessibility indicators that capture progress towards 15-minute city goals and that highlight the degree to which daily travel needs can potentially be fulfilled within local trips. 2. Analysing actual travel behaviour in relation to the 15-minute city objectives to describe the degree to which different societal groups fulfil their daily travel needs by local trips in practice and to understand current barriers to meeting sustainable travel objectives. 3. Develop a modelling environment that allows the development of future scenarios that enhance accessibility by the provision of transport infrastructure, adapting travel behaviour or (re)developing the city.

Path choices by transport network users depend in part on decisions made in response to foreseeable circumstances at the start of the trip and in part on choices made as the trip unfolds. In the case of transit networks, on-trip choices may depend on factors such as which line arrives first at a particular stop or platform, whereas in the case of traffic networks, on-trip choices may be influenced by factors such as an encountered queue, the state of traffic signals, or information received by the driver. In transit assignment, the effects of on-trip decisions are accommodated through the definition of a set of elemental paths that may be optimal (referred to as a hyperpath) and strategies for on-trip choices. In traffic assignment, the stochastic user equilibrium principle has been used to capture the effects of on-trip choices and different user preferences. However, the usual forms of stochastic assignment (C-logit, path size logit, cross-nested logit or probit) generally place few or ad hoc constraints on the set of feasible paths and assume that all feasible paths have a non-zero probability of use. Choice of path is assumed to be the result of random utility variation rather than the outcome of a choice strategy in the face of unfolding circumstances as the trip takes place. To this extent, the treatment of route choice behaviour is more sophisticated in transit than in traffic assignment. Where congestion and service disruptions are a prominent feature, it is necessary to take time-dependency into account in defining hyperpaths. The objective of this proposal is to extend the concepts of hyperpath and strategy-based decision-making to dynamic transit networks, traffic networks and multi-modal networks and then assess the benefits of so doing.

Reducing carbon emissions is one of the most important goals to prevent the world from disastrous future consequences of climate change. The transport sector requires specific actions, as it proves most difficult to decarbonise and transport emissions are again increasing. However, efforts to foster mobility behaviour change largely fail, as future national reduction goals are too unspecific for citizens to induce a sense of personal responsibility and engagement. MyFairShare builds on studies exploring the applicability of sufficiency principles to change mobility habits, e.g. through individual mobility budgets. Experiences show that transport emissions might be effectively reduced by limiting allowances for carbon-intensive transport modes, but would only be acceptable if the individual share of allowances is perceived as fair. MyFairShare combines and expands relevant knowledge, data and models to construct a scheme for fair distribution of individual mobility budgets, and identifies effective policy strategies. The potential will be tested in six Living Labs in different context situations, defined by scale (community - municipal - (trans-)national) and scope (citizen level- transport management level - strategic development level). The resulting policy toolkits and guidelines support the introduction of socially acceptable mobility budgets in different countries on different governance levels, improving urban accessibility and transport equity.