This paper examines key challenges in supporting passenger use of augmented and virtual reality headsets in transit. These headsets will allow passengers to break free from the restraints of physical displays placed in constrained environments such as cars, trains and planes. Moreover, they have the potential to allow passengers to make better use of their time by making travel more productive and enjoyable, supporting both privacy and immersion. However, there are significant barriers to headset usage by passengers in transit contexts. These barriers range from impediments that would entirely prevent safe usage and function (e.g. motion sickness) to those that might impair their adoption (e.g. social acceptability). We identify the key challenges that need to be overcome and discuss the necessary resolutions and research required to facilitate adoption and realize the potential advantages of using mixed reality headsets in transit.

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There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

This paper presents a powerful approach to evaluating public technologies by capturing and analysing pedestrian traffic using computer vision. This approach is highly flexible and scales better than traditional ethnographic techniques often used to evaluate technology in public spaces. This technique can be used to evaluate a wide variety of public installations and the data collected complements existing approaches. Our technique allows behavioural analysis of both interacting users and non-interacting passers-by. This gives us the tools to understand how technology changes public spaces, how passers-by approach or avoid public technologies, and how different interaction styles work in public spaces. In the paper, we apply this technique to two large public displays and a street performance. The results demonstrate how metrics such as walking speed and proximity can be used for analysis, and how this can be used to capture disruption to pedestrian traffic and passer-by approach patterns.