An Anthropocentric and Enhanced Predictive Approach to Smart City Management

Davide Carneiro,António Amaral,Mariana Carvalho,Luís Barreto

doi:10.3390/smartcities4040072

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.

Highlights

Over the course of the 20th century, the population living in cities has increased from 220 million to nearly 2.8 billion, and according to the available forecasts, that figure will increase to nearly 6.9 billion in 2050, which will be close to almost 70% of the world’s population [1,2].The social and technological challenges that this entails led to the emergence of the so-called Smart Cities (SC)
The main goal that motivated this work was to define an architecture for Smart Cities that could meet certain criteria: to be holistic, by integrating different sources of data into a single repository; to be context-aware, by storing data in a connected way that resembles the physical relationships between infrastructures, devices and locations in the city; to be anthropocentric, by supporting services that focus on the citizen; and to be predictive, so that future states of the city could be anticipated
We aimed at validating the prototype of the proposed architecture, as well as the services developed, namely those based on predictive models

Summary

Introduction

The social and technological challenges that this entails led to the emergence of the so-called Smart Cities (SC). Aside from the technological enablers, the emergence of Smart Cities was motivated by the search for a solution to the problems associated with the exponential growth of urbanization [4]. Due to their potential implications in urban planning and design, sustainability, social digitalization, and cities’ smart governance embedding practices [5,6], Smart Cities have been attracting an unprecedented amount of attention among different stakeholders, especially those within academia, industry, and public policy making

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