Barriers to the Effective Implementation of AI in Predicting Public Works

Authors

DOI:

https://doi.org/10.62161/sauc.v11.5978

Keywords:

Artificial Intelligence, Urban Planning, Public Works, Prediction, Public Data Quality, Data Sharing, Smart Cities

Abstract

Public data quality issues often hinder AI applications in urban planning, affecting model applicability, effectiveness, and results. A case study on predicting public works impacted by network infrastructure and its city-wide impact is presented in this paper. This case has served to identify barriers and limitations to AI adoption in this subdomain, allowing to inform a set of recommendations to improve public data production and sharing, paving the way for future AI modelling in public works prediction. By addressing these challenges, cities can unlock the full potential of AI-driven urban planning and decision-making.

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References

Anwar, M. R., & Sakti, L. D. (2024). Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning. ResearchGate. https://www.researchgate.net/publication/380557083_Integrating_Artificial_Intelligence_and_Environmental_Science_for_Sustainable_Urban_Planning DOI: https://doi.org/10.34306/itsdi.v5i2.666

Batty, M. (2013). The New Science of Cities. The MIT Press. https://www.jstor.org/stable/j.ctt9qf7m6 DOI: https://doi.org/10.7551/mitpress/9399.001.0001

Bibri, S. E., Huang, J., Jagatheesaperumal, S. K., & Krogstie, J. (2024). The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environmental Science and Ecotechnology, 20, 100433. https://doi.org/10.1016/j.ese.2024.100433 DOI: https://doi.org/10.1016/j.ese.2024.100433

Chaturvedi, V., & de Vries, W. T. (2021). Machine Learning Algorithms for Urban Land Use Planning: A Review. Urban Science, 5(3), Article 3. https://doi.org/10.3390/urbansci5030068 DOI: https://doi.org/10.3390/urbansci5030068

Correia, C. R., & Roseland, M. (2022). Addressing Negative Externalities of Urban Development: Toward a More Sustainable Approach. Urban Science, 6(2), 38. https://doi.org/10.3390/urbansci6020038 DOI: https://doi.org/10.3390/urbansci6020038

Damioli, G., Van Roy, V., Vertesy, D., & Vivarelli, M. (2025). Is artificial intelligence leading to a new technological paradigm? Structural Change and Economic Dynamics, 72, 347-359. https://doi.org/10.1016/j.strueco.2024.12.006 DOI: https://doi.org/10.1016/j.strueco.2024.12.006

Delgado Enales, I. (2024). Advanced optimization and data modeling techniques to improve accesibility and thermal comfort in urban plannning. https://doi.org/10/73521

Delgado-Enales, I., Molina-Costa, P., Osaba, E., Urra-Uriarte, S., & Del Ser, J. (2022). Improving the Urban Accessibility of Older Pedestrians using Multi-objective Optimization. 2022 IEEE Congress on Evolutionary Computation (CEC), 1-8. https://doi.org/10.1109/CEC55065.2022.9870432 DOI: https://doi.org/10.1109/CEC55065.2022.9870432

D’Ignazio, C., & Klein, L. F. (2020). Data Feminism. The MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001 DOI: https://doi.org/10.7551/mitpress/11805.001.0001

Fernández Álvarez, L., & Garaizar Sagarminaga, P. (2024). La inteligencia artificial explicada a todos los públicos (castellano). A Fin de Cuentos. https://afindecuentos.com/libros/la-inteligencia-artificial-explicada-a-todos-los-publicos/

Gao, R. X., Krüger, J., Merklein, M., Möhring, H.-C., & Váncza, J. (2024). Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions. CIRP Annals, 73(2), 723-749. https://doi.org/10.1016/j.cirp.2024.04.101 DOI: https://doi.org/10.1016/j.cirp.2024.04.101

Garg, P. K. (2021). Overview of Artificial Intelligence. En Artificial Intelligence. Chapman and Hall/CRC. DOI: https://doi.org/10.1201/9781003140351-2

Gong, Y., Liu, G., Xue, Y., Li, R., & Meng, L. (2023). A survey on dataset quality in machine learning. Information and Software Technology, 162, 107268. https://doi.org/10.1016/j.infsof.2023.107268 DOI: https://doi.org/10.1016/j.infsof.2023.107268

Hamann, K. R. S., Bertel, M. P., Ryszawska, B., Lurger, B., Szymański, P., Rozwadowska, M., Goedkoop, F., Jans, L., Perlaviciute, G., Masson, T., Fritsche, I., Favaro, T., Hofer, A., Eisenberger, I., Gutschi, C., Grosche, C., Held, J., Athenstaedt, U., & Corcoran, K. (2023). An interdisciplinary understanding of energy citizenship: Integrating psychological, legal, and economic perspectives on a citizen-centred sustainable energy transition. Energy Research & Social Science, 97, 102959. https://doi.org/10.1016/j.erss.2023.102959 DOI: https://doi.org/10.1016/j.erss.2023.102959

He, W., & Chen, M. (2024). Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning. Buildings, 14(3), Article 3. https://doi.org/10.3390/buildings14030835 DOI: https://doi.org/10.3390/buildings14030835

Korobeinikova, A., Danilina, N., & Teplova, I. (2024). Planning Public Space Climate Comfortability: A GIS-Based Algorithm for the Compact Cities of the Far North. Land, 13(11), Article 11. https://doi.org/10.3390/land13111763 DOI: https://doi.org/10.3390/land13111763

Koutra, S., & Ioakimidis, C. S. (2023). Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges. Land, 12(1), Article 1. https://doi.org/10.3390/land12010083 DOI: https://doi.org/10.3390/land12010083

Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., & Lee, I. (2018). Artificial Intelligence in the 21st Century. IEEE Access, 6, 34403-34421. https://doi.org/10.1109/ACCESS.2018.2819688 DOI: https://doi.org/10.1109/ACCESS.2018.2819688

Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain Intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications, 23(2), 368-375. https://doi.org/10.1007/s11036-017-0932-8 DOI: https://doi.org/10.1007/s11036-017-0932-8

Mashhood, M., Salman, H., Amjad, R., & Nisar, H. (2023). The Advantages of Using Artificial Intelligence in Urban Planning – A Review of Literature. STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH, 5(2), Article 2. https://doi.org/10.52700/scir.v5i2.125 DOI: https://doi.org/10.52700/scir.v5i2.125

Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., & Harmouch, H. (2025). The effects of data quality on machine learning performance on tabular data. Information Systems, 132, 102549. https://doi.org/10.1016/j.is.2025.102549 DOI: https://doi.org/10.1016/j.is.2025.102549

Molina-Costa, P. (2024). La planificación urbana en la era de la digitalización. In Ciudad digital Conectividad y (des)aceleración. CATARATA. https://www.catarata.org/libro/ciudad-digital_150546/

Moore, G. E. (2006). Cramming more components onto integrated circuits, Reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff. IEEE Solid-State Circuits Society Newsletter, 11(3), 33-35. https://doi.org/10.1109/N-SSC.2006.4785860 DOI: https://doi.org/10.1109/N-SSC.2006.4785860

Park, C., No, W., Choi, J., & Kim, Y. (2023). Development of an AI advisor for conceptual land use planning. Cities, 138, 104371. https://doi.org/10.1016/j.cities.2023.104371 DOI: https://doi.org/10.1016/j.cities.2023.104371

Pericault, Y., Viklander, M., & Hedström, A. (2023). Modelling the long-term sustainability impacts of coordination policies for urban infrastructure rehabilitation. Water Research, 236, 119912. https://doi.org/10.1016/j.watres.2023.119912 DOI: https://doi.org/10.1016/j.watres.2023.119912

Rjab, A. B., Mellouli, S., & Corbett, J. (2023). Barriers to artificial intelligence adoption in smart cities: A systematic literature review and research agenda. Government Information Quarterly, 40(3), 101814. https://doi.org/10.1016/j.giq.2023.101814 DOI: https://doi.org/10.1016/j.giq.2023.101814

Sanchez, T. W., Fu, X., Yigitcanlar, T., & Ye, X. (2024). The Research Landscape of AI in Urban Planning: A Topic Analysis of the Literature with ChatGPT. Urban Science, 8(4), Article 4. https://doi.org/10.3390/urbansci8040197 DOI: https://doi.org/10.3390/urbansci8040197

Shulajkovska, M., Smerkol, M., Noveski, G., & Gams, M. (2024). Enhancing Urban Sustainability: Developing an Open-Source AI Framework for Smart Cities. Smart Cities, 7(5), Article 5. https://doi.org/10.3390/smartcities7050104 DOI: https://doi.org/10.3390/smartcities7050104

Shumway, R. H., & Stoffer, D. S. (s. f.). Time Series Analysis and Its Applications.

Urra-Uriarte, S., Herranz-Pascual, K., Molina-Costa, P., Martin Roncero, U., & Glodeanu, A. (2024). An Age-Friendly Neighbourhood Index as a Long-Term Urban Planning Decision-Making Tool. Urban Science, 8(3), Article 3. https://doi.org/10.3390/urbansci8030127 DOI: https://doi.org/10.3390/urbansci8030127

Urrutia-Azcona, K., Usobiaga-Ferrer, E., De Agustín-Camacho, P., Molina-Costa, P., Benedito-Bordonau, M., & Flores-Abascal, I. (2021). ENER-BI: Integrating Energy and Spatial Data for Cities’ Decarbonisation Planning. Sustainability, 13(1), Article 1. https://doi.org/10.3390/su13010383 DOI: https://doi.org/10.3390/su13010383

Villanueva-Merino, A., Urra-Uriarte, S., Izkara, J. L., Campos-Cordobes, S., Aranguren, A., & Molina-Costa, P. (2024). Leveraging Local Digital Twins for planning age-friendly urban environments. Cities, 155, 105458. https://doi.org/10.1016/j.cities.2024.105458 DOI: https://doi.org/10.1016/j.cities.2024.105458

Wu, S. Z. (2025). The AI City. Springer Nature. https://doi.org/10.1007/978-981-96-2560-4 DOI: https://doi.org/10.1007/978-981-96-2560-4

Xiao Ting Cheng, Ming Wang, & XingQi Fan. (2025). Generation of Personalized Urban Public Space Color Design Scheme Assisted by Artificial Intelligence | International Journal of High Speed Electronics and Systems. https://www.worldscientific.com/doi/10.1142/S0129156425401615 DOI: https://doi.org/10.1142/S0129156425401615

Zheng, Y., Lin, Y., Zhao, L., Wu, T., Jin, D., & Li, Y. (2023). Spatial planning of urban communities via deep reinforcement learning. Nature Computational Science, 3(9), 748-762. https://doi.org/10.1038/s43588-023-00503-5 DOI: https://doi.org/10.1038/s43588-023-00503-5

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Published

2025-12-01

How to Cite

Usobiaga Ferrer, E., Molina-Costa, P., Ispizua, B., & Izkara, J. L. (2025). Barriers to the Effective Implementation of AI in Predicting Public Works. Street Art & Urban Creativity, 11(7), 201–219. https://doi.org/10.62161/sauc.v11.5978

Issue

Vol. 11 No. 7 (2025): Special Issue: Smart City World Congress

Section

Special Issue SmartCityExpo

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