Development of Resource-Demand Networks for Smart Cities 5.0

Kozhevnikov, Sergey; Skobelev, Petr; Přibyl, Ondřej; Svítek, Miroslav

doi:10.1007/978-3-030-27878-6_16

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Studies regarding how to efficiently use sensor data in such simulation models representing cities have been conducted (Crooks et al, 2021;Clemen et al, 2021;Guastella et al, 2019). Using general principles, functionality, and the architecture of the digital multi-agent platform, a Resource-Demand (RD) 10.1163/21971927-bja10044 | triple helix (2023) 1-38 Model has also been conceptualised for developing smart cities (Kozhevnikov et al, 2019), which views a smart city as a complex system, which is alive and constantly evolving and adapting. Using this approach, the model describes how a city can self-organise and evolve by utilising a set of Key Performance Indicators (KPI) like business, recreation, comfort, transport, environment, goods availability, medical care, education and prices for services.…”

Section: Learning Approachesmentioning

confidence: 99%

Learning in Cities from Within and Across Cities: A Scoping Review

Banerjee,

Petersen

2023

Triple Helix

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Cities evolve rapidly while providing both opportunities and posing challenges. To cope with the emerging behaviours of cities, contextual innovations and development are essential. Driving innovations through the learning of contextual knowledge in cities is crucial. In this study, we explore relevant studies to identify the frameworks for human-centric innovations in cities that consider learning from within and/or across cities. We analyse how learning in cities has been addressed in those studies and find that even though some aspects of learning in cities have been studied, a comprehensive framework for how cities can learn as an innovation ecosystem is missing. Based on the findings of a scoping review and insights from the theories of the Triple, Quadruple and Quintuple helices of innovation, we present a high-level conceptual model for cities as innovation ecosystems. The model is aimed to support sustainable human-centric development through the understanding of city learning through multilevel interactions and feedback.

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