While partially automated vehicles can provide a range of benefits, they also bring about new Human Machine Interface (HMI) challenges around ensuring the driver remains alert and is able to take control of the vehicle when required. While humans are poor monitors of automated processes, specifically during 'steady state' operation, presenting the appropriate information to the driver can help. But to date, interfaces of partially automated vehicles have shown evidence of causing cognitive overload. Adaptive HMIs that automatically change the information presented (for example, based on workload, time or physiologically), have been previously proposed as a solution, but little is known about how information should adapt during steady-state driving. This study aimed to classify information usage based on driver experience to inform the design of a future adaptive HMI in partially automated vehicles. The unique feature of this study over existing literature is that each participant attended for five consecutive days; enabling a first look at how information usage changes with increasing familiarity and providing a methodological contribution to future HMI user trial study design. Seventeen participants experienced a steady-state automated driving simulation for twenty-six minutes per day in a driving simulator, replicating a regularly driven route, such as a work commute. Nine information icons, representative of future partially automated vehicle HMIs, were displayed on a tablet and eye tracking was used to record the information that the participants fixated on. The results found that information usage did change with increased exposure, with significant differences in what information participants looked at between the first and last trial days. With increasing experience, participants tended to view information as confirming technical competence rather than the future state of the vehicle. On this basis, interface design recommendations are made, particularly around the design of adaptive interfaces for future partially automated vehicles. INDEX TERMS Intelligent vehicles, autonomous vehicles, interface, eye tracking, information requirements, HMI.

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Trust has been shown to play a key role in our ability to safely use autonomous vehicles; hence the authors used the Ideas Café to explore the factors affecting trust in autonomous vehicles. The Ideas Café is an informal collaborative event that brings the public together with domain experts for exploratory research. The authors structured the event around factors affecting trust in the technology, privacy and societal impact. The event followed a mixed methods approach using: table discussions, spectrum lines and line ups. 36 participants attended the Ideas Café event held at the Coventry Transport Museum in June 2017. Table discussions provided the key findings for Thematic Analysis as part of Grounded Theory; which found, contrary to current research trends, designing for the technology's integration with society as equally important for trust as the vehicle design itself. The authors also reported on the emergent high level interface guidelines.

The shift to electric vehicles has brought about the potential to reduce the environmental damage caused by road transport. However, several challenges prevent wider adoption of electric vehicles, such as: a lack of charging facilities, long charging times, limited range, and the inconvenience of cable charging. These barriers are more pronounced for taxis, which generally cover longer distances than regular cars and have fewer opportunities for recharging. This research aims to evaluate wireless charging for range extended electric taxis, as a strategy to minimise these challenges and facilitate the electrification of fleets. A mixed methods approach, combining quantitative vehicle tracking with qualitative interviews and focus groups with drivers and local authority representatives, provided an understanding of ‘facilitators’ and ‘barriers’ to the introduction of wireless chargers in London and Nottingham, UK. Results indicated that current wired charging infrastructure does not facilitate recharging opportunities during taxi working hours, causing longer shifts or lower earnings. Drivers reported running on a range extender petrol engine once the battery is depleted, limiting the environmental benefits of electric taxis. We conclude that wireless chargers could facilitate the increased driving range of existing electric taxis if installed where drivers stop more often. The results support the implementation of opportunistic, short but frequent charging boosts (known as choko-choko) as part of policies to alleviate the barriers to the introduction of wireless charging of electric taxis, and foster more sustainable means of road transportation.

This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Mixed methods research requires data integration from multiple sources. Existing techniques are restricted to integrating a maximum of two data sources, do not provide step-by-step guidance or can be cumbersome where many data need to be integrated. We have solved these limitations through the development of the extended Pillar Integration Process (ePIP), a method which contributes to the field of mixed methods by being the first data integration method providing explicit steps on how to integrate data from three data sources. The ePIP provides greater transparency, validity and consistency compared to existing methods. We provide two worked examples from health sciences and automotive human factors, highlighting its value as a mixed methods integration tool.

Transport behaviour has evidently changed following the COVID-19 pandemic, with lower usage across multiple modes of public transport and an increasing use of private vehicles. This is problematic as private vehicle use has been linked to an increase in traffic-related air pollutants, and consequently global warming and health-related issues. Hence, it is important to capture transport mode choice preferences following the pandemic, so that potential service changes can be made to address the lower usage. In total, 1138 respondents took part in an online discrete choice experiment methodology to quantify the utility of public transport service attributes in decision making around the choice of public transport. The data resulted in the development of three models using a multinomial logit model in R. For respondents on personal or commuting journeys, the mode of transport had no effect on utility. Results found that fare cost was the most important factor driving transport mode preference, when a range of choices were available. Following this, keeping fare cost consistent, faster journey times were preferred to stronger access to transport (i.e., through the provision of more bus stops/stations). The provision of operational relevant information to the journey was only significantly valued by commuters and travellers who could claim their journey as a business expense. Finally, when cost became less relevant (i.e., for travellers on expensed journeys), there was a significantly strong preference for taxi and road vehicle transport over all other transport modes. The results from this empirical research are discussed and the implications of recent transport policy are discussed, and recommendations of public transport service design are made.

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