Research Article
Open access
Published on 21 March 2024
Download pdf
Delli,H.J. (2024).AI for smart cities opportunities and promising directions.Advances in Engineering Innovation,5,44-48.
Export citation

AI for smart cities opportunities and promising directions

Hasan Jalo Delli *,1,
  • 1 University of South Florida

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2977-3903/5/2024041

Abstract

The transformative potential of Artificial Intelligence (AI) in the realm of smart cities is an evolving landscape of innovation and challenges. This research undertook a comprehensive exploration of AI's impact, harnessing both quantitative and qualitative methodologies. Detailed analyses were performed on data from select smart cities globally, focusing on sectors such as energy, traffic, health services, and waste management. Additionally, perceptions and experiences of urban stakeholders were captured through interviews. The results solidified AI's tangible benefits in enhancing urban life quality, while also bringing forth concerns about data privacy, algorithmic biases, and socio-economic implications. The study concludes with a call for holistic AI frameworks, heightened public engagement, and interdisciplinary collaborations to ensure the ethical and sustainable evolution of AI-integrated smart cities.

Keywords

Smart Cities, Artificial Intelligence, Data Privacy, Algorithmic Bias, Urban Development

[1]. Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F. (2014). Current trends in Smart City initiatives: Some stylised facts. Cities, 38, 25-36.

[2]. Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., ... & Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36(5), 748-758.

[3]. Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80-91.

[4]. Liu, H., Tian, Z., Zhang, Y., & Lu, H. (2018). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 20(7), 2627-2639.

[5]. Zhang, D., Li, S., Li, D., & Chen, L. (2017). Urban energy consumption prediction using a combination of the ARIMA model and neural networks based on big data. Journal of Cleaner Production, 165, 730-742.

[6]. Chauhan, S., Vig, L., & Chauhan, D. S. (2016). Anomaly detection in road traffic using visual surveillance: A survey. arXiv preprint arXiv:1606.00796.

[7]. Liang, F., Guan, P., Wu, X., Huang, D., & Guo, J. (2019). Review of the applications of artificial intelligence in public health surveillance. Journal of healthcare engineering, 2019.

[8]. Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2020). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 136, 109782.

[9]. Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274-279.

[10]. Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101, 63-80.

[11]. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14.

Cite this article

Delli,H.J. (2024).AI for smart cities opportunities and promising directions.Advances in Engineering Innovation,5,44-48.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content

About volume

Journal:Advances in Engineering Innovation

Volume number: Vol.5
ISSN:2977-3903(Print) / 2977-3911(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).