| Title | An Anthropocentric and Enhanced Predictive Approach to Smart City Management |
|---|---|
| ID_Doc | 40495 |
| Authors | Carneiro, D; Amaral, A; Carvalho, M; Barreto, L |
| Title | An Anthropocentric and Enhanced Predictive Approach to Smart City Management |
| Year | 2021 |
| Published | Smart Cities, 4, 4 |
| Abstract | Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city's assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly. |
| https://www.mdpi.com/2624-6511/4/4/72/pdf?version=1634814482 |
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