FIALKA SOBKOVÁ, L., M. ČERTICKÝ, and Š. JIRÁČEK. APPLICATION OF TRANSPORTATION BIG DATA TO SUPPORT DECISION-MAKING FOR ARCHITECTURE TEAMS: PROCESSES AND EXPERIENCES FROM TWO CASE-STUDIES. In: MAMBRETTI, S. and J.L. MIRALLES I GARCIA, eds. WIT Transactions on Ecology and the Environment. Sustainable City 2019 - 13th International Conference on Urban Regeneration and Sustainability, Valencia, 2019-10-01/2019-10-03. Southampton: WIT Press, Ashurst Lodge, 2019. p. 639-651. 238. ISSN 1743-3541. ISBN 978-1-78466-355-1. DOI 10.2495/SC190551.
Increasing demands on big data and smart solutions are visible in most branches of human activity. This movement is also present in the field of architecture and urban design. To build proper tools and information structures, which deliver the required information to architects and urban designers, the further use of big data is necessary. The focus of this paper is the description of how architects use the big data report in the design process of the medium-scale public space renewal projects. We analyzed the usage of big data analysis during the design processes of two public space renewal projects. The Prague Institute of Planning and Development commissioned two pilot studies for Klárov square and Revoluční street, where the big data report was a part of the project bases. The provided report was extracted from the agent-based simulation model of multimodal mobility of Prague. We combined in this agent-based model the data from mobile phone traces, statistical offices, open street maps, public transport timetables, travel diary surveys, foursquare and cadastral offices. The big data report contains data about residents and passersby – about their quantity, education, financial income, gender, age, marital status, number of children and household members, economic activity, mode of transport and type of activity. We also visualized the spatial distribution of paths and destinations. The experiences of both architecture teams had many common points. They considered the reported sociologic structure of the residents and tourists for the public space design as irrelevant. They would appreciate more detailed information about the transportation flow dependent on time of day. Furthermore, they would expect the possibility to test different transportation scenarios in big data-based models as a part of the design process. Both teams would prefer to cooperate with data specialists as direct members of the design team instead of receiving passive reports.
