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As cities resume life in public space, they face the difficult task of retaining outdoor activity while decreasing exposure to airborne viruses, such as the novel coronavirus. Even though the transmission risk is higher in indoor spaces, recent evidence suggests that physical contact outdoors also contributes to an increased virus exposure. Given that streets constitute the largest percentage of public space in cities, there is an increasing need to prioritise their use to minimise transmission risk. However, city officials currently lack the assessment tools to achieve this. This article evaluates the extent to which street segments are associated with spatiotemporal variations of potential exposures of pedestrians to virus transmission. We develop a multi-component risk score that considers both urban form and human activity along streets over time, including (a) an assessment of pedestrian infrastructure according to the average width of pavements, (b) a measure of accessibility for each street based on its position in the street network, (c) an activity exposure score that identifies places along streets where exposure could be higher and (d) an estimate of the number of pedestrians that will pass through each street during weekdays and weekends. We use Amsterdam in the Netherlands as a case study to illustrate how our score could be used to assess the exposure of pedestrians to virus transmission along streets. Our approach can be replicated in other cities facing a similar challenge of bringing life back to the streets while minimising transmission risks. [ABSTRACT FROM AUTHOR]

Crime has major influences in urban life, from migration and mobility patterns, to housing prices and neighborhood liveability. However, urban crime studies still largely rely on static data reported by the various institutions and organizations dedicated to urban safety. In this paper, we demonstrate how the use of digital technologies enables the fine-grained analysis of specific crimes over time and space. This paper leverages the rise of ubiquitous sensing to investigate the issue of bike theft in Amsterdam—a city with a dominant cycling culture, where reportedly more than 80,000 bikes are stolen every year. We use active location tracking to unveil where stolen bikes travel to and what their temporal patterns are. This is the first study using tracking technologies to focus on two critical aspects of contemporary cities: active mobility and urban crime. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) are an emergent technology. The very few studies focusing on how people perceive and react to autonomous vehicles are mainly based on online surveys. Although important, these studies measure people's opinions on AVs, but not their actual reaction when using them. In this paper, we assess users' perceptions of AVs using an immersive virtual reality experiment. We modeled autonomous boats that are intended to be deployed in Amsterdam, the Netherlands. We compared users' reactions to manned and unmanned boats in a virtual reality environment. In total, 30 participants were exposed to a Virtual Reality (VR) simulation, where we collected skin conductance data, which was compared with a participant's responses to a questionnaire regarding perceived risk, stress, and trust toward manned and unmanned boats. The study finds that autonomous boats do not substantially influence the experienced stress, trust, or perceived risk in comparison with human-controlled boats. This article also shows the gains that virtual reality can bring to studies of the social appropriation of technologies. Results show the need for objective, quantitative data regarding the societal acceptance toward autonomous systems to better understand how these technologies will be appropriated. [ABSTRACT FROM AUTHOR]

Amsterdam is a culturally rich city attracting millions of tourists. Popular activities in Amsterdam consist of museum visits and boat tours. By strategically combining them, this paper presents an innovative approach using waterborne autonomous vehicles (WAVs) to improve the museum visitation in Amsterdam. Multi-source urban data including I Amsterdam card data and Instagram hashtags are used to reveal museum characteristics such as offline and online popularity of museums and visitation patterns. A multi-objective model is proposed to optimize WAV routes by considering museum characteristics and travel experiences. An experiment in the Amsterdam Central area was conducted to evaluate the viability of employing WAVs. By comparing WAVs with land transportation, the results demonstrate that WAVs can enhance travel experience to cultural destinations. The presented innovative WAVs can be extended to a larger variety of points of interest in cities. These findings provide useful insights on embracing artificial intelligence in urban tourism. [ABSTRACT FROM AUTHOR]