AI in Smart Cities Development: A Perspective of Strategic Risk Management

Eduardo Rodriguez, John Edwards

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The purpose of this paper is to present the components of an Artificial Intelligence (AI)-based system design for better city strategic risk control. Several smart cities have made open data available to support various stakeholders’ interests: web pages and data tables can guide citizens and businesses and in many cases enable them to carry out service transactions. The data provides a resource for city studies, the development of indicators in support of city policymakers, and city administrators. However, the problem of administering a city requires scaling up to an integrated view, so such
systems are at an early stage of development. This study presents example cases where a dynamic and predictive system for a city has been created based on the use of AI, to guide city administrators based on possible future events. The cases cover crime, road traffic management/accidents, education, and health events, using data from three North American cities: Baltimore, Chicago, and Toronto. Together the cases serve as both a proof of concept for, and a test of, the approach needed to create an integrated predictive system. In this paper, the AI models are described along with all the steps in the approach, from data gathering to the creation of a system to support decisions. The main points are related to how risks can be mitigated and controlled using AI in strategy and policy formulation and implementation to improve citizens’ life. Data patterns can drive decisions, such as: crime seasonality supporting the planning of patrols and human presence in areas of potential issues; understanding traffic levels reducing the time people spend in cars; coordination of investment
providing a better use of the city’s resources. The examples presented illustrate the creation of a range of dynamic and adaptive predictive systems based on AI that are fed by the city-generated open data which contributes to the control of
the services provided by the city. Together they illustrate the feasibility of progress towards fully integrated systems.
Original languageEnglish
Title of host publicationProceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019
EditorsPaul Griffiths, Mitt Nowshade Kabir
Place of PublicationReading, UK
PublisherAcademic Conferences and Publishing International
Pages277-286
ISBN (Electronic)978-1-912764-44-0
ISBN (Print)978-1-912764-45-7
Publication statusPublished - 31 Oct 2019
EventEuropean Conference on the Impact of Artificial Intelligence and Robotics - Oxford, United Kingdom
Duration: 31 Oct 20191 Nov 2019

Conference

ConferenceEuropean Conference on the Impact of Artificial Intelligence and Robotics
CountryUnited Kingdom
CityOxford
Period31/10/191/11/19

Fingerprint

Risk management
Artificial intelligence
Crime
Websites
Accidents
Railroad cars
Education
Systems analysis
Smart city
Strategic risk
Health
Planning
Industry

Keywords

  • artificial Intelligence
  • smart cities
  • strategic risk
  • analytics
  • dynamic and predictive performance systems

Cite this

Rodriguez, E., & Edwards, J. (2019). AI in Smart Cities Development: A Perspective of Strategic Risk Management. In P. Griffiths, & M. Nowshade Kabir (Eds.), Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019 (pp. 277-286). Reading, UK: Academic Conferences and Publishing International.
Rodriguez, Eduardo ; Edwards, John. / AI in Smart Cities Development : A Perspective of Strategic Risk Management. Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019. editor / Paul Griffiths ; Mitt Nowshade Kabir. Reading, UK : Academic Conferences and Publishing International, 2019. pp. 277-286
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Rodriguez, E & Edwards, J 2019, AI in Smart Cities Development: A Perspective of Strategic Risk Management. in P Griffiths & M Nowshade Kabir (eds), Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019. Academic Conferences and Publishing International, Reading, UK, pp. 277-286, European Conference on the Impact of Artificial Intelligence and Robotics , Oxford, United Kingdom, 31/10/19.

AI in Smart Cities Development : A Perspective of Strategic Risk Management. / Rodriguez, Eduardo; Edwards, John.

Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019. ed. / Paul Griffiths; Mitt Nowshade Kabir. Reading, UK : Academic Conferences and Publishing International, 2019. p. 277-286.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Edwards, John

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N2 - The purpose of this paper is to present the components of an Artificial Intelligence (AI)-based system design for better city strategic risk control. Several smart cities have made open data available to support various stakeholders’ interests: web pages and data tables can guide citizens and businesses and in many cases enable them to carry out service transactions. The data provides a resource for city studies, the development of indicators in support of city policymakers, and city administrators. However, the problem of administering a city requires scaling up to an integrated view, so suchsystems are at an early stage of development. This study presents example cases where a dynamic and predictive system for a city has been created based on the use of AI, to guide city administrators based on possible future events. The cases cover crime, road traffic management/accidents, education, and health events, using data from three North American cities: Baltimore, Chicago, and Toronto. Together the cases serve as both a proof of concept for, and a test of, the approach needed to create an integrated predictive system. In this paper, the AI models are described along with all the steps in the approach, from data gathering to the creation of a system to support decisions. The main points are related to how risks can be mitigated and controlled using AI in strategy and policy formulation and implementation to improve citizens’ life. Data patterns can drive decisions, such as: crime seasonality supporting the planning of patrols and human presence in areas of potential issues; understanding traffic levels reducing the time people spend in cars; coordination of investmentproviding a better use of the city’s resources. The examples presented illustrate the creation of a range of dynamic and adaptive predictive systems based on AI that are fed by the city-generated open data which contributes to the control ofthe services provided by the city. Together they illustrate the feasibility of progress towards fully integrated systems.

AB - The purpose of this paper is to present the components of an Artificial Intelligence (AI)-based system design for better city strategic risk control. Several smart cities have made open data available to support various stakeholders’ interests: web pages and data tables can guide citizens and businesses and in many cases enable them to carry out service transactions. The data provides a resource for city studies, the development of indicators in support of city policymakers, and city administrators. However, the problem of administering a city requires scaling up to an integrated view, so suchsystems are at an early stage of development. This study presents example cases where a dynamic and predictive system for a city has been created based on the use of AI, to guide city administrators based on possible future events. The cases cover crime, road traffic management/accidents, education, and health events, using data from three North American cities: Baltimore, Chicago, and Toronto. Together the cases serve as both a proof of concept for, and a test of, the approach needed to create an integrated predictive system. In this paper, the AI models are described along with all the steps in the approach, from data gathering to the creation of a system to support decisions. The main points are related to how risks can be mitigated and controlled using AI in strategy and policy formulation and implementation to improve citizens’ life. Data patterns can drive decisions, such as: crime seasonality supporting the planning of patrols and human presence in areas of potential issues; understanding traffic levels reducing the time people spend in cars; coordination of investmentproviding a better use of the city’s resources. The examples presented illustrate the creation of a range of dynamic and adaptive predictive systems based on AI that are fed by the city-generated open data which contributes to the control ofthe services provided by the city. Together they illustrate the feasibility of progress towards fully integrated systems.

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KW - smart cities

KW - strategic risk

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M3 - Conference contribution

SN - 978-1-912764-45-7

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BT - Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019

A2 - Griffiths, Paul

A2 - Nowshade Kabir, Mitt

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ER -

Rodriguez E, Edwards J. AI in Smart Cities Development: A Perspective of Strategic Risk Management. In Griffiths P, Nowshade Kabir M, editors, Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019. Reading, UK: Academic Conferences and Publishing International. 2019. p. 277-286