The City of Amsterdam has the ambition to offer its citizens independent and equal participation as much as possible. However, people with disabilities encounter a high variety of barriers when moving around or participating in the city. We believe recent advancements in artificial intelligence can be crucial in finding and solving these bottlenecks. To guarantee a meaningful, useful and usable application of AI, we have partnered with World Enabled, who will guide us through the process. "Recent studies suggest that 25% of all people encounter barriers in cities based on age or disability. With the right partnerships and tools we can accelerate the development and deployment of new urban tech and G2G solutions at scale. We look forward to embarking on this journey and leveraging the combined technical capabilities of Intelligent Amsterdam and World Enabled. - Dr. Victor Pineda, President of World Enabled The advancements in the field of Artificial Intelligence potentially open up a whole range of new possibilities in tackling this topic, allowing for new ways to measure accessibility with a generic and scalable approach. In the next few years, we will be using the powers of Artificial Intelligence to help cities become more accessible, during this presentation we will describe what we are working on and showcase some of the research projects we have been working on

Walking in most Indian cities is a practice carried out with caution, especially for women. While there has been urgent discourse on the challenges faced in the practice of walking, there is a recognition of the waterfront promenades of Mumbai, as a place that offers favourable walking environments for women. It provides opportunities to partake in different typologies of walks, choosing from diverse, inclusive environments that could, for example, offer spaces to pause and connect with urban nature, interact with fellow pedestrians, or to loiter and immerse oneself with the rhythms of the city. However, this engagement becomes possible only when there is a feeling of safety. Through site-specific observations along waterfronts, as well as an analysis of interviews conducted with 25 women, the research explores the relationship between environmental characteristics- physical and social dimensions of specific settings along waterfronts- and the dynamics of safety. There is an aim to understand safety in relation to instances of crime, but also to extend this definition- connecting safety to practices of urban belonging, sharing, commonality, care for one another other, and care for the environment. The result is the identification of urban forms and atmospheres along waterfront promenades, that play a key role in the perception and creation of place for women, using narratives. The study addresses the core importance of walkability for health, liveability and sustainability, but also as a means for women to participate in urban life.

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding pedestrian activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal pedestrian activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic pedestrian activities on streets, which complements existing street classification frameworks, which focus on the static characteristics of the street segments. Our results show that a street classification framework based on temporal pedestrian activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning.