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1 Urbanismus a územní rozvoj

1 2023

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Využití umělé inteligence pro analýzu pohybu lidí ve veřejném prostoru. Případová studie Mariánské náměstí v Praze

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Zdráhalová J. , Kurilla L. , Prášek P. , Berka Z.

nr 5

26-35

The search for a relationship between the nature of public space and the ways in which people use that space is one of the standard tasks of urban design (Sitte, 2012; Whyte, 1980; Gehl, 1971; Lynch, 1960). Human activities in space and people's reactions to the urban and architectural characteristics of a place can be monitored through mental maps and interviews (Lynch, 1960; Benda et al., 1978), photographs and films (Whyte, 1980; Gehl, 1971), or through local experiences and observations, such as following people's routes by tracing a trampled footprint in the snow (Sitte, 2012). The knowledge gained is typically used to create an informed design of a public space that addresses, in particular, the usability of the space, safety issues, and the elimination of collisions between road traffic and people's residential activities. This text presents an urban experiment that tested the possibilities of involving artificial intelligence in mapping activities in public space. It presents the results of a four-year research, supported by TA ČR NCK TN01000024, which used CCTV camera recordings to monitor activities in a given public space. Specifically, we focused on Mariánské Square in the capital city of Prague, where five CCTV cameras that continuously monitored the space of the square were placed. Data collection took place in two cycles, each lasting four days. The first one took place in October 2019 before the reconstruction of the space, the second one a month later - after the road traffic in the square was regulated and new furnishing was added (Prague chairs, a large table, mobile flowerpots and concrete blocks to prevent the traffic). In both cases, the space was recorded from Thursday to Sunday to capture both the weekdays and the weekend. The collected data from the CCTV cameras was converted into trajectories using a neural network, which was used to create heatmaps. The heatmap shows the density of mobile and stationary activities of people in the square area. The physical space was described in terms of material design, height characteristics (curbs, stairs), location of furnishings, parking spaces and traffic organization. The heatmap intensity was compared with the physical characteristics of the space in order to find connections, relationships and a deeper understanding of the patterns of people's behaviour in the space. The comparison of the results of both observations showed how the specific design of the square influences the frequency of people in the space, increases residential activities and also leads to the use of elements in the space for new and unintended activities (for example, the use of concrete blocks as benches). The use of artificial intelligence to collect and interpret people's movements in public space has shown benefits that conventional participant observation does not provide. These include the objectivity of the collected data not burdened by the personalities of the observers, the comparability of data from different time periods, the possibility of accurately collecting data from large areas of public space, the possibility of accurately locating the trajectories of people in space, and the representation of dynamic patterns of space use. Furthermore, the data collected in this way allows for detailed interpretations of the relationship between people's trajectories, space characteristics and other environmental influences such as temperature, shading, noise pollution, etc., however, these are beyond the scope of this paper.

Tento text zkoumá možnosti využití umělé inteligence pro interpretaci aktivit ve veřejném prostoru měst. Aplikací neuronové sítě na kamerové záznamy, které po čtyři dny snímaly aktivity na Mariánském náměstí v Praze, jsou vygenerovány trajektorie jednotlivých uživatelů prostoru. Promítnutím jejich přesné polohy do půdorysu náměstí získáváme tzv. heatmapy pohybu osob, tedy zobrazení hustoty trajektorií. Výhodou tohoto přístupu je získání velkého množství informací o pohybu v celé ploše náměstí v dlouhém časovém úseku. Oproti standardnímu zkoumání metodou zúčastněného pozorování nebo pouhým počítáním osob, které projdou přes určený práh, jsou tyto informace nezatížené osobou výzkumníka, jsou přesné a mapují prostor jako celek. Pro komplexnější obrázek o využití prostoru jsme vyvinuli sémantický anotátor – nástroj, pomocí kterého výzkumník přiřazuje na základě kamerových záznamů aktivity osob k jednotlivým trajektoriím. Větší automatizace celého procesu by umožnila provádět složitější úlohy o hledání vztahu mezi daným místem a aktivitami lidí v něm.